Fields of the Cell

Fields of the Cell Editors: Daniel Fels1, Michal Cifra2, Felix Scholkmann3 1

Institute of Botany, University of Basel, Switzerland; Institute of Photonics and Electronics, The Czech Academy of Sciences, Prague, Czech Republic 3 Bellariarain 10, Zurich, Switzerland 2

Research Signpost, T.C. 37/661 (2), Fort P.O., Trivandrum-695 023 Kerala, India

Published by Research Signpost 2015; Rights Reserved Research Signpost T.C. 37/661(2), Fort P.O., Trivandrum-695 023, Kerala, India E-mail IDs: [email protected] [email protected]; [email protected] Websites: http://www.ressign.com http://www.trnres.com http://www.signpostejournals.com http://www.signpostebooks.com Editors Daniel Fels Michal Cifra Felix Scholkmann Managing Editor S.G. Pandalai Publication Manager A. Gayathri Research Signpost and the Editors assume no responsibility for the opinions and statements advanced by contributors ISBN: 978-81-308-0544-3

Contents Prologue

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Introduction

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Chapter 1 The evolution of the biological field concept Antonios Tzambazakis

1 1

Chapter 2 The field and the photon from a physical point of view Pierre Madl and Stephane Egot-Lemaire

29 29

Chapter 3 Detection and measurement of biogenic ultra-weak photon emission Pierre Madl

55 53

Chapter 4 Equilibrium and far-from equilibrium states Claudio Rossi, Pierre Madl, Alberto Foletti and Chiara Mocenni

71 67

Chapter 5 The origin and the special role of coherent water in living systems Emilio Del Giudice, Vladimir Voeikov, Alberto Tedeschi and Giuseppe Vitiello

95 91 Emilio Del G

Chapter 6 The photon source within the cell Ankush Prasad and Pavel Pospíšil

113 109

Chapter 7 Photon emission in multicellular organisms Eduard Van Wijk, Yu Yan and Roeland Van Wijk

131 127

Chapter 8 Electromagnetic cell communication and the barrier method Daniel Fels

149 145

Chapter 9 Coherence and statistical properties of ultra-weak photon emission Christian Brouder and Michal Cifra

163 159

Chapter 10 Cellular electrodynamics in kHz–THz region Michal Cifra

189 185

Chapter 11 Investigating encounter dynamics of biomolecular reactions: long-range resonant interactions versus Brownian collisions Jordane Preto, Ilaria Nardecchia, Sebastien Jaeger Pierre Ferrier and Marco Pettini

215

Chapter 12 Synchrony and consciousness Thilo Hinterberger, Cigdem Önal-Hartmann and Vahid Salari

229 227

Chapter 13 Cytoskeletal electrostatic and ionic conduction effects in the cell Douglas Friesen, Travis Craddock, Avner Priel and Jack Tuszynski

247 245

Chapter 14 Morphogenetic fields: History and relations to other concepts Lev V. Beloussov

271 269

Chapter 15 Endogenous bioelectric cues as morphogenetic signals in vivo Maria Lobikin and Michael Levin

283 283

Chapter 16 Electromagnetic resonance and morphogenesis Alexis Pietak

303 303

Epilogue

321

Acknowledgements

321

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Prologue While still a high-school pupil, I became strongly interested in information transfer among organisms that was not based on vision, sound, or any material component. Yet, studying biology there were no lectures satisfying this primary interest of mine but what I learned was, nonetheless wonderful. After graduation and leaving university, I thought that biology basically describes random collisions of material components while phenomena like field forces, entanglement or synchronicity (sensu Pauli and Jung) are ignored. I felt an urge to find out more and picked up my academic education again in the field of evolutionary biology focusing at that time during a PhD and later a post-doc on parasites and the ecology of their transmission. During that period of my academic education I came across studies about photons emitted by cells (but differently from bioluminescence) in very low numbers and by all types of cells. These works made me increasingly aware of the electrodynamic world of the cell being much more than photosynthesis, membrane potentials or electrostatics of protein folding. Cells are filled with electrodynamic fields that exist because of charged molecules, ions and chemical reactions, all of which move through the confined space of a cell. Fields interact with charged particles and consequently the endogenous electrodynamic fields of the cell are assumed to play a significant role in organizing cell processes and form. We believe that the exploration of the fields of the cell will inevitably change our understanding of life processes. We learn about a combination of a reductionist’s “from genotype to phenotype” (bottom-up principle) view with a “from field to form” (top-down principle) view where thinking of cause and effect will still make sense but where rather watching the process as such – the reciprocal causality between matter and field – will be the core. The following collection of papers on electrodynamic fields and biological processes is intended as a small step in the direction of forming an integrated view of biological cells.

Daniel Fels Basel, February 2, 2015

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Introduction The role of bioelectromagnetic and photobiological factors in functional and structural organization of biosystems – Towards a new paradigm in the understanding of life In recent years, an increasing number of results from basic research support the view that biophysical, in particular bioelectromagnetic and photobiological factors are fundamental for the functional and structural organization of biosystems. Despite the relatively well-known and wellresearched bioelectromagnetic factors (endogenous currents, static and dynamic electric and magnetic fields as well as electromagnetic fields) in the context of biological function, photobiological factors (endogenous photon emissions) have become the focus of research as an additional entity influencing and guiding life processes. Are we heading towards a new paradigm in the understanding of life? On the one hand, photobiological factors can be regarded as a subclass of bioelectromagnetic factors since light or a photon is a specific type of electromagnetic field (i.e. in the range from the lower part of the ultraviolet spectrum [UV, approx. 10 nm] to the upper part of the infrared spectrum [IR, approx. 1 mm]). On the other hand, the distinction between bioelectromagnetic and photobiological factors is justified because of the need to use different physical frameworks for their description, i.e. classical electro-dynamics and quantum physics, respectively. Whereas traditionally biochemistry is regarded as the basis for both intrasystemic (within a biosystem) and intersystemic (between biosystems) organization, many facts support the notion that biophysics plays a significant role as well. The research activities regarding this aspect can be categorized into four classes (see Table 1). The categorization is based on the insight that the inter- and intrasystemic organization of a biosystem (i.e. a cell or a whole multicellular organism) or a collective of biosystems, respectively, stand also strongly under the influences of bioelectromagnetic and/or photobiological factors. The research fields that investigate the bioelectromagnetic and photobiological processes involved in the intra- and intersystemic organization are bioelectromagnetics and photobiology, respectively. It should be noted that both research fields traditionally focus mainly on how exogenous electromagnetic or photonic factors can have an impact on biosystems while they include also research on endogenous (i.e. within a biosystem) electromagnetic or photonic factors. In the present book we mainly focus on endogenous bioelectromagnetic and photobiological processes.

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Bioelectromagnetics

Intrasystemic

Intersystemic

Dielectrophoretic forces or electric signalling within a cell

Electric signalling between cells

Prominent researchers in the field of bioelectromagnetics are for example: H.S. Burr, R.O. Becker, B. Nordenström, M. Levin, R.H. Funk, L.F. Jaffe, K.B. Hotary, R.B. Borgens, J. Pokorny, M. Cifra, J. Tuszynski

Photobiology

Photonic signalling between fluorophores in a cell

Photonic signalling between cells

Prominent researchers in the field of photobiology are for example: A.G. Gurwitsch, V.P. Kaznacheev, G. Albrecht-Bühler, D. Fels, F.A. Popp, R. Van Wijk, E.P. Van Wijk

Table 1. Categorization of four research sub-fields involved in the understanding of bioelectromagnetic and photobiological processes of intrasystemic and intersystemic organization of biosystems. Note that only examples of research topics are given in the research sub-fields.

While the role of bioelectromagnetic factors in the organization of biosystems is a well-established field of research, the understanding of photobiological factors involved in life processes is still at the beginning and has the potential of a very important topic for future biophysical research. To understand the significance of photobiological factors with regard to biosystems, let us first draw our attention to the role of light in the biosphere. Light ranging from 200–1000 nm (i.e. the UV, visible and near IR range) comes from the biggest local energy source we can have access to, the Sun. While these higher frequencies are shining on Earth, only the lower IR frequencies leave the Planet, radiating back to Space. Part of the energy is absorbed by the exposed surface of the geosphere and another part is absorbed by the biosphere. What happens with the light that Life uses? Organisms with light receptors can use the light (also when reflected from surfaces) as a signal and organize themselves in accordance to their needs: e.g., for growth, phototaxis, attack, escape or mating. In fact, a great deal of the dynamics of ecosystems is based on photosynthesis and vision. Hence, light plays not only a decisive role as energy deliverer for the biosphere and its ecosystems but also a fundamental role in the way these systems are functionally organized. We know this. And yet, we would like to lead the reader into the cell(s), asking whether the ”cellular ecosystem“ is also (at least partly) organized by light or fields, respectively. Let’s step back. We know what happens with sunlight in the kingdom of the plants: photosynthesis transforms electromagnetic energy into energy of excited electrons, the latter being captured in sugar molecules (C6H12O6) that form from carbon dioxide (CO2) and water (H2O) with the associated release

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of oxygen (O2). These sugar molecules (i) are stored immediately (as starch) or (ii) used as raw material for producing (together with, e.g. phosphate and nitrogen) other organic molecules such as lipids, amino acids or more complex molecules such as, e.g., porphyrines or DNA, or (iii) they go directly into a chemical pathway that kind of inverts photosynthesis: glycolysis and mitochondrial respiration. In both plant and animal cells, glycolysis and mitochondrial respiration serve to gain back the electromagnetic energy stored not only in (the binding electrons of) sugar but also in all the molecules built. This process is finalized in the mitochondria when protons (H+) and electrons (e-) as well as carbon dioxide (CO2) get separated from the molecules. In the respiratory chain of the inner mitochondrial membrane the electronic energy is used for pumping the protons into the intermembrane space leading to extremely high concentrations of positively charged protons. This will cause two phenomena. One is the flow (along an electric and concentration gradient, respectively) of these protons back into the matrix – along ATPase – where they combine together with the electrons and O2 to H2O while adenosine diphosphate (ADP) + phosphate (PO4-) are catalyzed to adenosine triphosphate (ATP). ATP is then used as a source of activation energy in many kinds of processes involving muscular and neuronal activity as well as molecule synthesis where some of its energy goes into these processes. However, some of its energy goes also into the environment as (low) thermal energy contributing to the thermal bath of the cytoplasm. Yet, the other phenomenon is that a tremendous electrostatic potential builds up between the (hundreds and more of) mitochondria per cell and their surrounding cytoplasm. We could stop there, claiming that this is all we can say about the energy delivery: it comes from ATP and the thermal bath, which latter contributes to the chances of collision of reaction partners. But when biomolecules are fed into the mitochondria oxygen is not only used for water formation, it is also used in redox reactions and is involved in the production of so called reactive oxygen species (ROS). What is interesting here is that reactions of ROS and following products lead to the release of photons in the visible range. Can these photons have effects in the cell, delivering activation energy or functioning as a signal inducing particular chemical reactions, or are they just an insignificant byproduct? Moreover, the tremendous H+ concentrations in the mitochondrial intermembrane space organize charged molecules due to their high electric potential. Charged atoms, i.e. ions in general, play an important role regarding life processes. They build electric fields used for signal transduction in the nervous system, they organize space in the context of morphogenesis, induce gene expression and they are used for osmosis, the water content regulation of cells. Surprisingly, latest studies in water research point out that water itself can build electric fields. If we recall that charged particles cannot

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remain inert in an electric field and that molecules show to great extent dipolar functional groups, it is a small step to imagine or rather conclude that the fields of the cells organize the structure of the cells. Research on the impact of magnetic fields on life organization finds evidence for ferromagnetic particles and magnetosensitive chemical reactions in bacteria or birds, enabling these organisms to respond to the structure of the earth magnetic field. We can envision that the fields of the cells give structure, hence playing an important role in the process of self-organization (Fig. 1).

Figure 1. Illustration of the major hypothesis of this book, namely that charged cells and molecules, respectively, induce electromagnetic fields that feed back on these cells and molecules where both the charged parts and the fields together build a system of causal reciprocity leading to observed cell system dynamics.

The goal of the present book is to introduce the role of endogenous biological electromagnetic and photonic factors influencing and guiding life processes. To this end, we invited experts in these research fields to write a review summarizing the research findings they gathered over many years. Each review obtained is one chapter of the present book. The first chapter introduces the history of the field concept in biology from the early 20th century to recent times covering all important milestones and related theories on non-equilibrium, non-linear and coherent behavior of biological systems. The second chapter explains the physical view on the electromagnetic field and photons. The electromagnetic spectrum is described as well as coherence, interference, resonance and interactions of an electromagnetic field with matter. Chapter three then gives an overview of detection techniques for ultraweak photon emission, such as observed from biological systems. Chapter four describes fundamental features of living system that are nonlinear and far from thermodynamic equilibrium: a rise of order from disorder. This is possible for cases where energy is flowing through the system and the system is able to dissipate the disorder (entropy) to its surroundings. Doing so, internal ordering of the system is achieved. In chapter five we come across experimental findings about water behavior in-

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dicating that water at hydrophilic interfaces is the simplest non-equilibrium system able to transform disordered to ordered energy. Hence, a theory of coherent water domains will be presented, explaining both, well-known and newly observed properties of water. It is concluded that water plays a central role in the dynamics of biomolecules and therefore also subsequently in the generation of the cellular electrodynamic field. One of the manifestations of living system’s non-equilibrium behavior is the permanent production of electron excitation in biomolecules leading to ultra-weak photon emission. Based on solid experimental evidence, chapter six explains the generation of electron excited chemical species due to free radical and reactive oxygen species reactions. Biological ultra-weak photon emission is of very general nature. It is detectable from every metabolically active biological species under suitable conditions. Chapter seven focuses on ultraweak photon emission from multicellular organisms, namely plants, tumor tissues and humans. It relates photon emission to development and structure as well as to tumor and normal cells comparing them with reference to growth properties. The eighth chapter explains the peculiar phenomenon of non-chemical influences between cell cultures through glass barriers. It is suspected that the non-chemical interaction between cell cultures is mediated by photon emission generated by cells. A special emphasis is given on confounding effects and the method itself in order to gain understanding about the function. As statistical properties of biological ultra-weak photon emission have been a source of controversy in past decades; chapter nine assesses available experiments studying optical coherence, quantum states and signal properties of biological ultra-weak photon emission. Chapter ten aims to explain that the electrodynamic activity of living cells involves a broad range of frequencies, namely from kilohertz to terahertz. These frequency ranges are related to electromechanical vibrations of subcellular structures. It is hypothesized that electrodynamic fields generated by such sub-cellular coupled oscillations contribute significantly to biological self-organization. Can biomolecules interact over long distances within a cell in order to find their chemical partners earlier than just by diffusion? Chapter eleven explains under which conditions such interactions can indeed take place using a model that involves resonant electrodynamic interactions of biomolecules. Rising to a macroscopic level, we come in chapter twelve across the collective activity of neurons giving rise to synchronous electric events in the brain. Such events are also known as preconditions for conscious acts to occur. Both, the potential role of photons emitted from neurons and being part of a time-sensitive signal flow within the brain (and the body) as well as synchronicity between distant brains are discussed with care. Coming down again to the microscopic world, the organization and signal processing on the level of single eukaryotic cell and especially neurons is cru-

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cially dependent on the cytoskeleton. In chapter thirteen the electric properties of microtubule and actin filaments are described as well as their possible role in cell signaling. Chapter fourteen then guides us from early biological field concepts to a modern theory of biological self-organization involving the coupling of fields from mechanic, electric and electromagnetic origin. Chapter fifteen provides us with condensed information on how tissue and cellular electric fields modulate the transcription of genes and shows, thus, basic principles of how cellular electric fields are coupled with biochemical pathways. The final chapter introduces the fact that biological objects, as any other dielectric objects, are able to store electromagnetic energy as cavity resonators under certain conditions. In resonators, electromagnetic energy is stored only in certain shapes (modes) at a certain frequency. Here it is proposed, that the spatial distribution of electromagnetic energy in such biological resonator provides conditions for symmetry breaking which guides differentiation and pattern formation during plant organ development. As every research field needs some critical amount of knowledge to be gathered until it can start to expand rapidly and bring applications, we wish the reader to be well-introduced and motivated starting his or her own research projects.

Daniel Fels Michal Cifra Felix Scholkmann

Research Signpost 37/661 (2), Fort P.O. Trivandrum-695 023 Kerala, India

D. Fels, M. Cifra and F. Scholkmann (Editors), Fields of the Cell, 2015, ISBN: 978-81-308-0544-3, p. 1–27.

Chapter 1

The evolution of the biological field concept Antonios Tzambazakis Institute of Anatomy and Clinical Morphology, Department of Human Medicine, Faculty of Health, Witten/Herdecke University, Alfred-Herrhausen-Straße 50, 58448 Witten, Germany Abstract: The dialogue with Nature about Time and Change has pointed to the functional synthesis of linearity with non-linearity since the times of Epicurus. The modern concepts of Quantum Coherence, Self-Organization, Deterministic Chaos and Reciprocal Causality in biology, also suggest an integration of linearity with non-linearity, of molecular with field aspects. In this review, we discuss the theories and experimental evidence on the existence, the role and the properties of a biological field, following the arrow of time in a scientific perspective that unites being with becoming, and particles with fields. Correspondence/Reprint request: Dr. Antonios Tzambazakis, Institute of Anatomy and Clinical Morphology Department of Human Medicine, Faculty of Health, Universitat Witten/Herdecke, Alfred-Herrhausen-Straße 50 58448 Witten, Germany. E-mail: [email protected]

1. Introduction Alexander Gurwitsch introduced for the first time the field concept into biology in 1912 (Gurwitsch, 1912). Gurwitsch tried to solve the biological problem of morphogenesis, and since chemical reactions do not contain spatial or temporal patterns a priori, he looked for a "morphogenetic field" as a supracellular dynamic law embracing all three levels of biological organization – molecular, cellular and morphological (Lipkind, 2006). In 1923, Gurwitsch evidenced the existence of ultraviolet mitogenic rays able to stimulate cell division in onion roots (Gurwitsch, 1923). Numerous replications of experiments confirmed the existence of "mitogenetic" radiation, a term given by Gurwitsch. Such phenomena of weak bioluminescence were later on termed Ultra-weak Photon Emission (UPE) or Biophotons in modern bioelectromagnetic field theories.

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Antonios Tzambazakis

The key influence to Gurwitsch can be traced to his friend and physicist Leonid Mandelstamm, who explained to Gurwitsch the advances in physics which led to the formulation of his morphogenetic field theory (Beloussov, 1997). The experimentalists of that era were under the influence of two primal explanatory concepts in conflict, preformism and epigenesis. Epigenesis originates from Aristotle (384 BC–322 BC) and preformism can be retraced to Hippocrates (460 BC–370 BC). Preformism and epigenesis were involved in the logical structure of the two main doctrines of developmental biology the former represented in the developmental mechanics founded by Wilhelm Roux in 1886 and the latter in modern vitalism founded by Hans Driesch in 1908 (Lipkind, 1992). While in Roux’s "mosaic theory of development" all parts of the organism develop along predetermined pathways and without influencing each other, in Driesch’s view the state of each part of the organism was dynamically co-determined by the neighboring parts and function was dependent on the position within the whole. The mechanistic/reductionist view of Roux (Roux, 1895) became dominant with genetic determinism. In 1926, Thomas Hunt Morgan had formally separated genetics from embryology (Morgan, 1926), while Morgan’s student Theodosius Dobzhansky redefined evolution as changes in gene frequency (Dobzhansky, 1937), giving rise to the modern evolutionary synthesis (Mayr, 2001) and to gene-centered views of evolution (Dawkins, 1976). However, recent discoveries in molecular biology revealed that the target of selection is the phenotype and not individual genes, since mutations and genetic recombinations occur in the context of all the constraints exerted on the organism, including those of the environment, where cooperativity may be as important as competition (Margulis and Sagan, 2002, Noble 2010). Vitalistic views faded out, but can still be found in such works as of Alistair Hardy (Hardy, 1965) that consider consciousness-related aspects of evolution. The common functioning principle in the controversy between preformism and epigenesis or mechanism and vitalism is that they are both teleological linear concepts, since the pathways of development are predetermined towards an end, either by the action of vital or mechanical factors following the top-down or the bottom-up causal chain respectively. In the history of science, the controversy between mechanism and vitalism is also reflected in the argument between particle and field theories (Jammer 1980/81, Bischof 1995). The linear view of a changeless guiding principle underlies these controversies. The pathway beyond linearity has its roots in the dilemma among being and becoming, expressed for the first time in the history of philosophy as the dilemma of Epicurus (342–270 BC). Hellenistic Philosophers believed that Philosophy, however technical and theoretical it may become, serves the interests of human happiness. If we understand the universe and its work-

The evolution of the biological field concept

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ings, they urged, we may more easily find a way to live in accordance with nature (Shields, 2003). The dilemma among the changing world of Heraclitus (535–475 BC) and the changeless world of Parmenides (5th century BC), led Epicurus to extend the deterministic atom theory of Democritus (460– 370 BC), and postulate that as atoms move through the void they clash and "swerve" from their otherwise determined paths and initiate new causal chains, providing indeterminism and novelty in the constantly changing material world, as also a basis for free will. However, the first suggestion providing a physical mechanism for stochastic phenomena based on a vectorizing natural principle, appeared with Darwin’s theory of biological evolution, which integrated necessity with chance, the determinism of adaptation with the indeterminism of varying conditions. Darwin’s concept of evolution by means of Natural Selection (Darwin, 1859) provided a mechanism able to explain the evolution of structural changes observed in nature, and connected life through the arrow of time. The biological field theory of Gurwitsch reflects a further conceptual shift from the linear view of particle-field dichotomy to a non-linear view of particle-field interactions, evident in the last version of his theory and the conception of Non Equilibrium Molecular Constellations, that can be considered as a the earliest formulation of the "dissipative structures" as described in modern self-organization theory (Nicolis and Prigogine, 1977). The concept of living organisms as non-equilibrium (open) systems proposed by Gurwitsch, suggested that living order exists in a state of non-equilibricity due to the action of biological fields (Gurwitsch 1923). Cooperative non-linear interactions are fundamental elements in modern biological field theories. Linearity is based on the absence of interactions among the parts of a system which latter is therefore determined by its initial conditions only and thus the whole can be reduced to its parts. Nonlinearity implies evolution and novelty as inherent properties of a system which parts are in constant dynamical interaction, thus not determined by initial conditions, and whose description cannot be deduced from the properties of its elements alone. The importance of interactions has gained considerable attention in self-organization and system biology approaches of the organism that consider reciprocal causality – simultaneous top-down and bottomup causal chains – through feedback control mechanisms among the whole and the parts of the organism (Gilbert and Sarkar 2000, Noble, 2006, Longo, 2012), taking into account the need for integration of epigenetic processes involved in gene regulation, and even non-genetic informational pathways as higher levels of cellular control (Strohman, 1997, Steele, 1998, Jablonka and Lamb, 2005). In addition to upward causation, cellular and tissue events i.e. physical/mechanical stresses occurring, may act reciprocally but also downwardly modifying the expression of these genes at a later time

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(Farge, 2003). Cancer can also result from altered chemical and physical interactions as a tissue-level phenomenon (Maffini et al., 2004), and this view of cancer, contrary to its mutation-centered counterpart, explains the fact that cancerous cells reverse their "malignant" properties when placed within normal tissues (Bussard et.al, 2010). Reciprocal causality is also evident in the approach of Gurwitsch focusing on the cooperative nonlinear interactions among molecules and field as elementary for the establishment of biological order. Gurwitsch suggested that the biological field is produced by the developing body itself and regarded the molecules as the objects of field action, he defined the field as a factor vectorizing a certain part of molecular excitation energy and stated that "the field is somehow associated with the molecules of chromatin, but only when they are chemically active" (Gurwitsch, 1944).

2. Historical development of theoretical concepts and experimental investigations 2.1 Biological field concepts The debate among Roux and Driesch stimulated the development of organismic biology and the concept of the biological field. The morphogenetic field of Gurwitsch gained support by Ross Harrison in 1918 (Harrison, 1918), and by Hans Spemann's "field of organization" in 1921 (Spemann, 1921). The organismic school tried to bridge biochemistry and morphology (Bischof, 2003), inspired the research for "organizer substances", "gradient fields", as also for "organizing principles", with corresponding research carried out by Ross Harrison, Charles Child, Paul Weiss (Weiss 1939), Joseph Needham (Needham, 1950), and Conrad Waddington (Waddington, 1957). The organismic view has led to systems biology that takes account complexity, emerging properties, multi-level functionality and reciprocal (top-down and bottom-up) causality, identified in the works of Claude Bernard, Ludwig Bertalanffy, Robert Rosen, Brian Goodwin, Scott Gilbert (Gilbert and Sarkar, 2000), and Denis Noble (Noble, 2006). In accordance to such views, Goodwin (Goodwin and Webster, 1982) as also Gilbert (Gilbert et al., 1996), have proposed a feedback loop between morphogenetic fields and gene activity.

2.2 Biophysical field concepts Although the first to introduce the field concept into biology, Gurwitsch did not share the same views with the organicists, and introduced the idea of a field of force "Kraftfeld", as a supracellular ordering principle corresponding to "spatial but immaterial factors of morphogenesis" (Beloussov, 1997). Scheminzky was the first to describe a cellular electromagnetic field (EMF) for the visible spectrum (Scheminzky, 1916) fol-


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lowed by the consecutive discovery of an EMF in the UV region (Ludwig, 1918, De Fazi, 1924). Independently, Gurwitsch did experiments with onion roots (Gurwitsch, 1923) and observed the involvement of UV emission triggering cell divisions suggesting the existence of a form of cellular radiation of an electromagnetic nature, which he termed "mitogenetic radiation". This study of Gurwitsch was the first to suggest that the emanation of light is not an incidental property of cells but might have relevance to signaling, and for this he was repeatedly nominated for Nobel Prize among 1923-1938. Gurwitsch and his colleagues used prisms for spectrum analysis and biological detectors (yeast cell cultures) for measurement of mitogenetic radiation (Gurwitsch and Gurwitsch, 1959). They found "finger-print" spectra for several enzymatic reactions and noticed spectral changes in light emission from cells following physiological changes. The apparent involvement of molecular events in these processes of mitogenetic radiation convinced Gurwitsch for the existence of collective states of molecules that he termed Non Equilibrium Molecular Constellations (NEMC), the first postulation of "cooperative phenomena" as the basis of life processes (Bischof, 2003). The NEMC’s indicated the presence of vectorial factors within cells, and thus Gurwitsch saw the mitogenetic rays as indirectly related to morphogenetic fields (Beloussov, 2012). Inspired by Gurwitsch’s work, mitogenetic research from several independent laboratories in Europe and USA gave rise to hundreds of papers, several books and dozens of reviews in the 1920s and 1930s (Beloussov 1997, Cifra 2009). Gurwitsch's work was supported by many Russian and Western workers but in view of the contradictory results obtained with biological detectors, some researchers introduced physical detectors such as the photographic plate and the UV-sensitive Geiger tube (Rajewsky, 1931, Siebert and Seffert, 1933, Audubert, 1938). Unfortunately, despite confirmation of his results by the later Nobel laureate D. Gabor, the scientific community forgot Gurwitsch’s work since the results using physical detectors were as variable as those obtained with biological detectors.

2.3 Early bioelectromagnetic field concepts However, mainly based on electrophysiological experimental work, some early proposals of bioelectromagnetic field concepts were made by Keller (1918), Laville (1925), Lakhovsky (1929), Burr (1935), Crile (1936), Lund (1947), Becker (1961), and Reich (Reich, 1948). Lakhovsky was the first to use high-frequency EM fields to treat cancer, with his multiple wave oscillator generating the whole spectrum frequencies from 750 KHz and up to the visible spectrum of 300 THz (Lakhovsky, 1929). The conclusions of Lund, based on decades of work in bioelectric potentials of plants and animals, are in line with Harold Saxton Burr who postulated that "the pattern of organization of any biological system is es-

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tablished by a complex electrodynamic field, which is in part determined by its atomic physicochemical components, and which in part determines the behavior and orientation of those components" (Burr et al., 1935, Burr, 1972). Only after World War II, when the invention of the photomultiplier tube (PMT) became available to biomedical researchers, the very sensitive and reliable PMT measurements proved the existence of cell radiation beyond any doubt (Bischof, 1998). The PMT allowed direct measurement of very small quantities of emitted light in the visible spectrum. The first PMT studies involved photon emission from green plants, including three species of algae, following irradiation with visible light (Strehler and Arnold, 1951). In 1954 Colli and Facchini (Colli and Facchini, 1954, Colli et al., 1955) detected weak visible region (400–700 nm) luminescence from seeds germinating in the dark. Only few of the mitogenetic researchers maintained that the organism acts as an emitter and receiver of electromagnetic frequencies for its regulation and structuring. In this tradition are the biologists Kaznacheev, Dombrovskii and Inyushin. In the 1960s several Russian groups headed by Tarusov, Konev, Vladimirov, Zhuravlev, and also other groups, studied the visible region luminescence from many plant and animal species. Zhuravlev and Seliger published the first working hypothesis for the Ultraweak Photon Emission from biological systems, the "Imperfection Theory", theorizing that UPE is produced as a result of metabolic "imperfections" based on the recombination of free radicals which are mostly oxidative chemical by-products. Konev was the first to employ the UV-sensitive PM tube to detect UV photon emission from living organisms. Konev's group by 1969 had already studied over 100 different species of organisms covering eight systematic types, including 13 algae, 9 yeast and 8 bacterial species (Konev, 1966, Ruth, 1979).

2.4 Modern bioelectromagnetic field concepts The accumulation of experimental evidence led to the breakthrough of modern bioelectromagnetic field theories in 1970, stimulated by Alexander Presman’s review on the work of Soviet bioelectromagnetics researchers (Presman, 1970). A number of developments that contributed to the modern bioelectromagnetic field theories include the concepts of non-equilibrium thermodynamics, negative entropy, self-organization, dissipative structures, biological coherence, and advanced quantum optics such as non-classical light and cavity quantum electrodynamics.

2.4.1 Non-equilibrium thermodynamics The introduction of the concept of living organisms as thermodynamically open non-equilibrium systems can be traced to Gurwitsch, Bauer, Vernadsky, and Bertalanffy. It was extended with the concept of negative entropy introduced by Schrödinger, further developed by Gyorgyi, and mathemati-


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cally formalized by Prigogine (Prigogine, 1947). Schrödinger maintained that living organisms preserve their high order by transferring entropy (low-organized energy) to the external world, while they feed on negative entropy (highly-organized energy) from the environment (Schrödinger, 1944), and Gyorgyi incorporated the negentropy concept in his theories on the excitation/de-excitation dynamics and the role of charge transfer in the organism (Szent-Gyorgyi, 1957).

2.4.2 Self-organization and coherence The concepts of Coherence and Self-organization established a sufficient theoretical basis able to integrate molecular and field aspects. At the International Conference "From Theoretical physics to Biology" at Versailles (Marois, 1969), Ilya Prigogine introduced his theory of dissipative coherent structures (1977 Nobel Prize in Chemistry), while Herbert Fröhlich introduced his theory of biological coherence. The proposal that living organisms are open systems able to "self-organize" under energy flow explained how biological systems use nutrition as an external pump to establish a stable state far from equilibrium, similarly to the Bénard convection cells or the (optically pumped) laser. In both cases, critical energy supply to one or a few collective modes of vibration results in a phase transition to a large-scale dynamic order in which all the molecules or atoms in the system move coherently, dominated by just a few degrees of freedom, while the non-thermal storage and stabilization of the modes lift the system to a stable state far from equilibrium.

2.4.3 Dissipative structures Prigogine explained self-organization processes on the basis of non-linear thermodynamics (Prigogine, 1945, Nicolis and Prigogine, 1977), where the flows of irreversible processes are non-linear functions of the thermodynamic forces (temperature, chemical concentration gradients). Non-linear thermodynamics, explained life phenomena such as the emergence of novelties, and the increase of complexity, on the basis of evolutionary "branching points", where the dynamical instability of the system rising at a threshold distance far from equilibrium, gives rise to an unpredictable and thus irreversible process of selection among a variety of stable modes of system function through the amplification of appropriate fluctuations, leading to the formation of dissipative structures. A dissipative system or structure is characterized by the spontaneous breaking of spatial and temporal symmetry, and the formation of complex structures in which, above a critical value, certain fluctuations are amplified and give rise to mesoscopic and macroscopic long-range supramolecular correlations among the interacting particles. The amplification of fluctuations results from feedback processes giving rise to coherent phenomena, such as the coupling between non-linear oscillators that could be biochemical oscilla-

8


tors, electromagnetic field and resonating biomolecular structures etc. In gene regulation for example, in the bacterium E.coli the lac genes are organized into an operon, a part of the bacterial genome. The lac operon synthesizes certain enzymes for metabolizing lactose to glucose, and which enzymes are affecting the transcription rate of lac operon exerting positive and negative feedback on their own synthesis. Thus, since products feedback on reactants, spatial symmetry breaks among them, and provides the capacity for selection towards various stable states of lactose and glucose distribution. The transition from the non-induced state to the induced state of oscillatory (periodic) enzyme synthesis and vice versa, breaks the temporal symmetry and is an allor-none phenomenon that depends on threshold values of various constraints such as the lactose and glucose average concentration levels in the cell. Above threshold values, feedback among the lac genes and their corresponding enzymes amplify positively or negatively the concentrations of lactose and glucose (amplification of fluctuations) providing self-regulation of the lactoseglucose metabolic pathway. Since the more complex a system is, the more are the types of fluctuations that put in danger its stability, and the expansion of communication (feedbacks, resonances) among system parts brings stability, it seems that evolution towards higher complexity is based on an antithesis among stability through communication and instability through fluctuations, which antithesis also determines the threshold of stability/sensitivity of the system. Prigogine pointed that the origin of life may be related to successive instabilities giving rise to successive amplifications that have led to a state of matter of increasing coherence (Prigogine, 1980), a concept later formalized as expansion of coherent states (Popp, 1992).

2.4.4 Coherent excitations The concepts of non-equilibricity and cooperativity were extended from the molecular level into the domain of bioelectromagnetics with Fröhlich’s concept of coherent excitations. According to Fröhlich, biological coherence results from long-range quantum mechanical phase correlations of the electromagnetic fields coupled to coherently excited particles in living systems (Fröhlich, 1968). Biological macromolecules act as collectively vibrating electric dipoles, forming an oscillating system in nonlinear interaction with a heat bath (essentially cell water and ions suspended in it). Spontaneous coherence arises through: (i) a critical supply of metabolic energy that allows the strong excitation of coherent longitudinal electric modes (of these macromolecules) which are stabilized by elastic deformations based on electron-phonon autocatalytic non-linear couplings, combined with (ii) non-linear supply of spectral energy where the energy fed into the branch of longitudinal electric modes gets channeled into one or a few vibration modes, finally leading to the establishment of coherent excitations, i.e. coherent oscillation states. Fröhlich suggested that biomolecular EMFs between 100–1000 GHz can originate from the strong


9

fluctuations of the 105 V/cm electric field produced by the cellular membrane. Fröhlich’s model was supported i.e. by the detection of non-thermal cellular radiation concerning the 0.3–10 µm IR (Fraser and Frey, 1968) and the 0.2–2 mm radiowave range (Gebbie and Miller, 1997), and by the so-called rouleaux formation of erythrocytes (Rowlands et al., 1981, Rowlands, 1982, Sewchand and Rowlands, 1983) where the adherence of equally charged cells can be satisfactorily explained only by a coupling of coherently oscillating cells.

2.4.5 Cancer and loss of coherence Fröhlich formulated later a related hypothesis (Fröhlich, 1988) wherein he deduced that the state of cancer is due to the loss of coherence and can possibly be restored by external irradiation with the correct resonant frequency, which was in fact supported from research on non-thermal biological effects of electromagnetic radiation by Webb (Webb et al., 1968, Webb et al., 1969) and Devyatkov (Devyatkov, 1973). Additional evidence came, on the one hand, from biophoton research claiming loss of coherence in cancer (as compared to healthy) cells (Schamhart et al., 1987, Scholz et al., 1988, Popp, 2009) and, on the other hand, from the application of Microwave Resonance Therapy (MRT)/ Extremely High Frequency Therapy (EHF) (Kositsky et al., 2001, Hyland, 2008) aiming to restore coherence (Sitko et al., 1994, Grubnik et al., 1998, Giuliani and Soffritti, 2010). Recent evidence revealed amplitude-modulated radiofrequencies to exhibit a tumor-specific resonant action that blocks the tumor growth by affecting gene expression and disrupting the mitotic spindle of cancer cells (Barbault et al., 2009, Costa et al., 2011, Zimmerman et al. 2012). These forms of non-invasive cancer therapy obviously can be linked with cancer diagnostics based on biophotons being an interesting cell characteristic for assessing the health state.

2.4.6 The cell membrane as source of cellular EMF generation Many research groups were inspired by Fröhlich’s suggestions (Cifra et al., 2011) trying to address the topic of coherently oscillating cell membranes, e.g. the model of EMF generation postulated by Devyatkov and coworkers (Devyatkov, 1973, Betskii et.al., 1988) is based on acoustic-electrical waves that exhibit a millimeter EM radiative component. Further, spectroscopic detection of cell membrane vibrational states report mechanical oscillations in the region of a few hundreds of Hz (Piga et al., 2007, Hargas et al., 2008, Koniar et al., 2009) as well as in the region of GHz (using Brillouin spectroscopy) and THz (using Raman spectroscopy) (Webb, 1980, Drissler and Santo, 1983, Del Giudice et al., 1985).

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2.4.7 The microtubule theory of cellular EMF generation The microtubule model postulated by Jiri Pokorny is based on the capability of cellular microtubules for generation of coherent oscillations in the region from 1 kHz to 100 GHz (Pokorný et al., 1997, Gu et al., 2009, Havelka et al., 2011). The electromagnetic field measured at living cells is assumed to be generated by non-linear elastic-electrical oscillations in microtubules, and mitochondria provide the main conditions for generation of coherent electrodynamic field by microtubules: (i) energy supply (ATP, GTP, liberation of non-utilized energy), (ii) strong static electric field able to shift microtubule oscillations into highly non-linear region, (iii) low damping of these oscillations through the formation of an ordered water layer around mitochondria. According to this model, cancer develops as a result of mitochondrial dysfunction (Warburg-effect), associated to suppression of the oxidative production of ATP and GTP (which plays an important role in the regulation of cell division) and its replacement by fermentative one, preceding the biochemical and genetic alterations taking place in the cancer transformation pathway. As a consequence, there is a decreased level of water ordering linked to damping of microtubule oscillations, and the power and coherence of the generated electrodynamic field are diminished (Pokorny et al., 2012). Supporting evidence concern detection of cellular electromagnetic field in the KHz-MHz region (Hölzel, 2001, Jelínek et al., 2009, Cifra, 2009), in the GHz region (Jelínek et al. 2005, 2007, Kucera, 2006), during cell mitosis (Jelínek et al., 1996, 1999), during cell replication phases (Pokorny et.al, 2001), mitochondrial ROS production as source of UPE (Hideg et al., 1991, Cadenas et al., 1980, Creath, 2008), mitochondria as source of near-IR EMFs (AlbrechtBuehler, 2000, Tuszynski and Dixon, 2001), and cancer diagnosis based on damping of microtubule oscillations (Pokorny, 2011).

2.4.8 The emanation from equilibrium Both, the amplification of fluctuations (as proposed by Prigogine) and coherent excitations (as proposed by Fröhlich) suggest that living organisms are able to amplify very weak signals. Further, both concepts point to non-linear processes of autocatalysis able to account for signal amplification, decrease of entropy, and the creation and establishment of a stable state far from equilibrium. However, at the Versailles conference (Marois, 1969), Fröhlich had discussed with Prigogine that the thermodynamical approach, since it cannot distinguish between a living organism and a non-living system of equal entropy state, provides the necessary but not the sufficient condition to explain selforganization phenomena. Fröhlich pointed out that a sufficient concept should include a quantum mechanical approach to explain the emergence of self-organization which is the essential characteristic of life, by providing a selective organization principle that refers to a few degrees of freedom only


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and, hence, is irrelevant to entropy. Recently, strong evidence has emerged that quantum coherence is playing an important role in photosynthesis (Engel et al., 2007, Collini et al., 2010, Panitchayangkoon et al., 2011), in bird navigation (Rodgers and Hore, 2009) and in the sense of smell (Franco et.al., 2011).

2.4.9 Biophoton Theory and DNA as source of cellular EMF generation The investigation of the nature of non-linear processes able to account for the emanation from equilibrium came from concepts such as the "Biophoton Theory" developed by Fritz-Albert Popp. In the period after 1970, research on UPE continued from groups associated with Slawinski (Poland), Boveris (USA), Quickenden (Australia), Inaba (Japan), and Popp (Germany). But, while most researchers assigned to the Imperfection Theory, Popp based the phenomenon on the principle of coherence and introduced the term "biophoton" (Popp, 1976) to distinguish these ultra-weak signals from classical bioluminescence and to, further, indicate a biological quantum phenomenon with bio-informational character. Popp developed applications on medical diagnostics (Popp, 2007) and on food quality (Vogel et al., 1998), as well as models on biological evolution (Popp et.al., 1992). According to the Biophoton Theory (Popp et al., 2003), UPE originates from a delocalized coherent electromagnetic field of optically coupled emitters and absorbers, stabilized and operating at the laser threshold far from equilibrium in the sense of Prigogine’s dissipative structures. The concept includes a "photon sucking" communication mechanism based on destructive interference by phase conjugation (Popp and Chang, 2000, Popp et al., 2007). Popp postulated a DNA laser-model being the main source of coherent ultraweak photon emission (Popp et al., 1979, Popp and Nagl, 1983). Electromagnetic and mechanical joint vibrations of photons and phonons in the DNA-lattice (DNA-polaritons) make it a perfect candidate for absorption, storage, and emission of coherent photons (Popp and Nagl 1986, Li et al., 1990, Li, 1992). This hypothesis is not proved besides a few supporting indirect evidence (Rattemeyer et al., 1981, Popp et al., 1984). There is no direct evidence for coherent lightemission from DNA and there exist arguments concerning the mathematical deduction of coherence from Popp’s experimental data (Salari and Brouder, 2011). Biophoton emission was detected in fractions of cells containing nuclei and DNA, but there was no emission in purified DNA, and neither in cell fractions containing cytosol, mitochondria, or ribosomes (Van Wijk et al., 1995, Van Wijk et al., 1997). However, studies on isolated nuclei revealed a light-induced UPE (Delayed Luminescence) of higher intensity than from whole cells, and point to the nucleus as a possible source of ultraweak photon emission (Niggli, 1996). Nevertheless, many experimental data are coming from the work of Popp and coworkers concerning UPE, from unicellular organisms to plants, animals and humans, in the IR, optical and

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UV spectrum, on spontaneous and light-induced UPE (Multi-author review, 1988, Popp and Beloussov, 2003, Kobayashi 2009), detection of light-induced UPE as a tool for investigating coherence properties (Popp et al., 1993, Yan et al., 2005, Musumeci et al., 2005a), and for cancer diagnosis (Takeda et al., 2004, Musumeci et al., 2005b, Popp, 2009). Furthermore, in line with Popp’s experimental evidence, there is a great abundance of data on non-chemical cellular electromagnetic interactions (Fels, 2009, reviews by Trushin, 2004 and Cifra, 2011).

2.4.10 The Electrosoliton theory of cellular EMF generation Solitons sensu Davydov (Davydov, 1985) are nonlinear states of electrons or AMID-1 excitations of peptide groups, bound with local lattice deformation of macromolecules (electron-phonon coupling), which can propagate along macromolecules, such as DNA and polypeptides, in the form of electrosolitons and provide energy and charge transport in metabolic processes without energy dissipation over macroscopic distances. The propagation of electrosolitons is accompanied by coherent radiation of electromagnetic waves in the form of superposition of harmonics with multiple frequencies (Brizhik and Eremko, 2003, Brizhik, 2008, Scordino et al., 2010). An alternating electromagnetic field of characteristic frequency is created due to this radiation. The field frequency is determined by soliton velocity and the main harmonic is estimated at 1 THz, in agreement with Fröhlich’s model (Fröhlich, 1988). The high-frequency alternating EMF of an electrosoliton affects electrosolitons in other chains. Due to the mutual influence via the emitted electromagnetic fields, electrosolitons tend to gain equal velocities. The longrange interaction among electrosolitons results in the dynamical synchronization of their motion and in the tuning of their radiation frequencies. Due to this fact, the radiation fields of electrosolitons become coherent, and the intensity of the total endogenous field is proportional to the number of electrosolitons. The bigger is the number of electrosolitons, the more intensive metabolic processes occur in a system. This synchronization has a selfregulating impact on the metabolic processes in a biosystem, and can also be stimulated by weak external EMF provided it has the frequency equal to the frequency of the endogenous EMF, constituting a plausible mechanism of microwave resonance therapy.

2.4.11 The Water Coherence Domains theory of cellular EMF generation A major role of cellular water in generation of electronically excited EMF is proposed by the concept of Water Coherence Domains (Voeikov and Del Giudice, 2009, Del Giudice et al., 2010, Brizhik et al., 2011). Each photon coming out from the EMF background (thermal bath and quantum vacuum) can resonate with water molecules present in its volume, so that a pile up of photons occurs within the volume (Coherence Domain) giving rise to a size-


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able EMF. At a given time Coherent Domains of Water oscillate at a given frequency attracting excited oscillating biomolecules able to resonate with the same frequency, and all molecules oscillate in phase with the selftrapped electromagnetic field. The attracted molecules react chemically among them and release energy in a non-thermal way through solitons, providing localized nonlinear charge transport and directionality to the supplied energy by "ratchet effect". Evidence supporting the existence of water CDs (Zheng et al., 2006, Chai et al., 2009, Pollack et al., 2009) reveals that water near interfaces exhibits quite different properties than bulk water. Evidence on the key role of cellular water in generation of electronically excited EMF has been reported for various processes related to ROS production (Vaks et al., 1994) and studies on "water respiration" in bicarbonate solutions (Voeikov et al., 2010). Supporting studies from human body investigations claim an infrared detection (3.5–5 µm) of the soliton states of longitudinal water CDs that are formed in water layers bound along aligned collagen fibers in connective tissues (Brizhik et al., 2009). In addition, polarized light microscope studies revealed that living organisms can display colours as a function of the coherent motions and coherent alignment of interfacial water and the accompanying molecular dipoles existing in liquid crystal mesophases within the tissues (Ho, 1993). There are also reports assigning a main informational capacity to water and concern detection of low frequency electromagnetic signals produced by aqueous structures surrounding bacterial DNA sequences (Montagnier et al., 2009a, 2009b), also proposing the use of spectra from aqueous DNA solutions for diagnostic purposes (Giuliani et al., 2011).

2.4.12 Contributing concepts Concepts that have contributed to modern biological field theories include the "bioplasma theory" postulated by Inyushin (Wolkowski, 1983), the physicochemical luminescence model of Slawinski (Multi-author review, 1988), and the electrochemical model of Pohl (Pohl, 1981, 1982). Supporting evidence of the Pohl model concern oscillating chemical reactions such as the Beloussov-Zhabotinski reaction (Voeikov et al., 2001a, 2001b, Epstein et al. 1996) and the indirect detection of cellular EMF radiation by dielectrophoresis (DEP) among 5 kHz–9 MHz (Pohl et al., 1985, Jandová et al., 1987, Holzel, 2001). A morphogenetic field concept has been synthesized by Lev Beloussov, where the coherence of the field is based on the cooperativity of mechanoelectrical and photon-producing molecular/supramolecular machines that transform low-grade energy stored in chemical bonds, into high grade mechanical, electrical and photonic energy, reducing the number of freedom degrees of the liberated energy through autocatalytic feedback couplings between them, regulated by macroscopic order parameters such as mechani-

14


cal stresses, electrical fields and factors regulating UPE temporal regimes (Beloussov, 2011). Importantly also there are evidence which show that the order in biosystems influences the statistical and coherence properties of light-induced UPE (Yan et al., 2005, Budagovsky et al., 2002). There are also some informational field models developed by Wolkowski, Laszlo, Rein, Pribram, Miller, Taylor, Bearden, Smith, Bohm, Tiller, and Ho (Bischof 1998, 2003), postulating biological effects of vector and scalar potentials mediating among the quantum vacuum and the quantum states of solid matter by controlling the phase of electromagnetic fields. Popp has also postulated that the property of "photon sucking" through destructive interference, is based on strong Casimir forces resulting from the interaction of vacuum fluctuations with rather narrow (local) vacuum states formed among double polarizable layers such as the DNA-helix or the cellmembrane (Popp et al., 1995). Also significant for the integration of the models on cellular EMF generation in the kHz–GHz range with the models on UPE (visible and UV range), was the proposal by Swain and Popp for a mechanism of upconversion of photons in the range of GHz frequencies that, under certain nonlinear conditions, could give rise to visible photons (Swain, 2006, Popp, 2006, Swain, 2008).

3. Towards quantum biology Judging from the overall picture, there is obviously no privileged causal level in biology, but cooperative interactions among the top-down and bottomup causal chains, field forces and physical/mechanical stresses that are working hand in hand with molecular events, a reciprocal causation among macroscopic, mesoscopic and microscopic levels, fields and molecules. One could suggest that an important step further in the development of the biological field concept, will need the integration and clarification of the main self-organization mechanisms that provide the network of nonlinear autocatalytic feedback couplings able to account for the system coherence-noncoherence transitions and the localized directional non-thermal charge transport processes. Necessary integration steps should involve a quantum field clarification of interactions among the main cell structures involved in generation of UPE and coherence e.g. mitochondria/microtubules, water, DNA, cytoplasmic membrane, and their participation in localized nonlinear charge transport processes (e.g. solitons) and in delocalized nonlinear information vector processes. It will be also necessary to experimentally clarify the phase coupling mechanism with the EMF background (heat bath and quantum vacuum). A crucial step forwards will be the coupling of the field informational vectors to the molecular informational networks of the cell. The non-local


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biophoton field seems to be coupled to various coherent and non-coherent states e.g. solitons, excitons, polaritons etc. but most of these interactions cannot be measured directly, since the only way of registering is the rough count outside of living material (Popp, 2002). Thus, an essential point of future studies should be the stimulus application of specific resonant frequencies and correlations with subsequent effects in molecular processes of cell regulation such as gene expression and metabolic interactions in multienzyme metabolic domains, that will need the combination of EMF application techniques like EHF/MRT technologies, with molecular techniques like genomics/ metabolomics/bioinformatics, as also far-field and near-field EMF detection techniques able to provide information on affected coherence properties of the system e.g. through assess of spectral distribution parameters. Future experiments should also focus in the up-conversion (or downconversion) mechanisms among RF/MW, IR and visible/UV photons. The combination of various detection/stimulus technologies in different spectra will be essential. As evidenced from the exponentially growing number of publications, research groups and conferences dedicated to the subject (Table 1), the biological field concept has evolved from the times of Gurwitsch following several routes and modulations as a result of the conceptual and technological scientific advances in the course of history until the modern era (Table 2), and has enriched Self-Organization Theory with the notion of Coherence, declaring that cooperativity, irreversibility and probability are intrinsic properties of Nature. These investigations represent a continuation of the dialogue about Time and Change, and provide an evolutionary view of Nature which goes beyond a mechanical timeless automaton as also beyond a meaningless random game, where biological processes no longer need to be based on the dichotomy between a transcendental controlling principle and a mechanically controlled inert organism.

Acknowledgements A.T. is deeply grateful to Prof. Fritz Albert Popp and the IIB (Neuss, Germany) for support and guidance, to Prof. Lev Beloussov and Prof. Larissa Brizhik for stimulating theoretical discussions, and to Dr. Daniel Fels and Dr. Michal Cifra for their essential help and cooperation during the preparation of this review.

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EMF frequency/ wavelength

Phenomena/ effects

onion roots, yeast cells, sea-urchin gastrula, (list not complete)

UV

cell cycle phases, enzymatic reactions, degradation radiation

yeast cells, onion roots, frog nerve excited nerve, (list not complete)

UV

various biochemical processes: yeast fermentation (list not complete)

Detection method

Organism/ structure

Biological detectors

Physical detectors: Photographic plate, UV-sensitive Geiger tube

Photomultipliers, Electron Multiplying CCD cameras

cellular EMF oscillations, mechanical vibrations, EM resonance of cell membrane (list not complete)

References reviews: Rahn (1936), Gurwitsch and Gurwitsch (1959), Ruth (1979) Scheminzký (1916) Ludwig (1918) Siebert (1933) Audubert (1938) Rajewsky (1931) Pohl and Pollock (1986), Jelínek et al. (2009), Cifra (2009) (list not complete)

alga, yeast cells (list not complete)

RW/MW (kHz, MHz, GHz)

frog muscle

0.2–2 mm (IR/THz)

non-thermal radiation

Gebbie and Miller (1997)

crab nerve

3–10 µm (IR)

non-thermal radiation

Fraser and Frey (1968)

bacteria, seeds, organs, eggs, plants, fungi, mitochondria, erythrocytes, leucocytes, insects, vertebrates (list not complete)

IR, visible, UV

various phenomena concerning spontaneous and photo-induced UPE: UPE synchronization, UPE patterns during cell cycle phases (list not complete) cellular EMF oscillations, variations during mitosis (list not complete) non-thermal mechanical vibrations, Raman shifts based on mechanical vibrations

Multi-author review (1988, 1992, 2003, 2008) (list not complete)

Dielectrophoresis

bacteria, mammalian cells, human leucocytes

5 kHz– 9 MHz

Pohl (1985), Pokorný (1990) (list not complete)

Spectroscopic techniques

mammalian cells, human liver, Escherichia coli (list not complete)

hundreds of Hz, THz

Electromagnetic distant interactions using: Photomultipliers, Barriermethod

mitochondria, ciliates, insects, vertebrates (list not complete)

IR, visible, UV

growth polarization, lipid peroxidation, proliferation (list not complete)

Galle et al. (1991), Fels (2009), Trushin (2004), Cifra (2011) (list not complete)

Effect of EMFs on biological systems using: EMF signal generators

yeast cells, plants, animals, humans (list not complete)

UV, RF, MW, spectral power density 10-6 – 10-21 W/Hz

tumor growth inhibition, analgesia, (list not complete)

Grubnik et.al. (1998), Costa et al. (2011), Zimmerman et.al. (2012) (list not complete)

Koniar et al.(2009), Del Giudice et al. (1985) (list not complete)

Table 1. Investigations of electromagnetic field properties of living systems.


Approach

Concept

17

Major concept authors - References to books and/or articles Del Giudice E. et.al. (2010). Water dynamics at the root of metamorphosis in living organisms. Water 2, 566–586. Brizhik L./Eremko A. (2003). Nonlinear model […] self-regulation of metabolic processes in biosystems, Electromagnetic Biology and Medicine 22:31-39 Pokorny J. et.al. (1997). Vibrations in microtubules. Journal of Biological Physics 23, 171-179.

Biophysical field

Ho M.W. (1993). The Rainbow and the Worm. World Scientific Popp F.A. (1976). Biophotonen, Verlag für Medizin Dr.Ewald Fischer, Heidelberg Fröhlich H. (1968). Long-range coherence and energy storage in biological systems, International Journal of Quantum Chemistry Vol.2, pp.641-649. Gyorgyi A.S. (1957). Bioenergetics. Academic Press. Burr H.S./Northrop F. (1935). The electro-dynamic theory of life, Quarterly Review of Biology, Vol.10 pp.322-333. Gurwitsch A.G. (1912). Die Vererbung als Verwirklichungsvorgang, Biologisches Zentralblatt, vol. 32, no. 8, pp. 458-486. Longo G. et.al. (2012). Is information a proper observable for biological organization? Progress in Biophysics and Molecular Biology 109, pp.108-114 Karsenti E. (2008). Self-organization in cell biology: a brief history. Nature Reviews Molecular Cell Biology. Mar.9(3): 255-62

Self-organiReciprocal causality

zation

Camazine S. et al. (2001). Self-Organization in Biological Systems. Princeton University Press. Ball P. (1999). The Self-Made Tapestry: Pattern Formation in Nature. Oxford University Press. Kauffman S. (1995). At Home in the Universe: The Search for Laws of SelfOrganization and Complexity. Oxford University Press. Haken H./Graham R. (1971). Synergetik, Umschau in Wissenschaft und Technik Heft 6, p. 191 Prigogine I./Nicolis G. (1967). On symmetry-breaking instabilities in dissipative systems. J. Chem. Phys. 46, 3542–3550. Soto A.M./Sonnenschein C. (2011). The tissue organization field theory of cancer. BioEssays 33, 332-340. Noble D. (2006). The music of life. Oxford University Press.

System theory

Kirschner M./Gerhart J. (2005). The Plausibility of Life: Resolving Darwin's Dilemma. Yale University Press. Gilbert S. et.al. (1996). Resynthesizing evolutionary and developmental biology. Dev. Biol. 173(2), 357–372 Goodwin B./Webster G. (1982). The origin of species: a structuralist approach, Journal Social Biol. Struct. 5 15–47 Bertalanffy L.V. (1968). General System Theory. Braziller. Rosen R. (1968). A Means Toward a New Holism. Science 161(3836): 34–35

Organicism, Holism, Morphogenetic field Top down causality

Waddington C. (1957). The Strategy of the Genes. Allen and Unwin. Needham J. (1950). Biochemistry and Morphogenesis. Cambridge University Press. Weiss P. (1923). Naturwissen. 11, 669 Spemann H. (1921). Die Erzeugung [...] taeniatus. Roux Arch. Entwicklungsmech. Org. 48: 533–570 Hardy A. (1965). The Living Stream. Harper and Row.

Vitalism

Driesch H. (1908). The Science and Philosophy of the Organism.The Gifford Lectures. Adam & Charles Black. Dawkins R. (1976). The Selfish Gene. Oxford University Press.

Bottom up causality

Reductionism

Dobzhansky T. (1937). Genetics and the Origin of Species, Columbia Univ. Press. Morgan T. H. (1926). The Theory of the Gene, Yale Univ. Press. Roux W. (1895). Einleitung. Roux’s Arch. Entwicklungsmech.Org. 1, 1–42.

Table 2. Evolution of concepts on biological order and causality approach.

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Webb, S.J., Stoneham, M.E., Fröhlich, H., (1977). Evidence for non-thermal excitation of energy levels in active biological systems. Physics Letters A 63, 407–408. Webb S.J., (1980). Laser-Raman spectroscopy of living cells. Physics Reports 60 (4), 201– 224. Weiss, P., (1939). Principles of Development. Cambridge University Press, Cambridge. Wolkowski Z.W. Sedlak W., Zon, J. (1983). The utility of bioelectronics and the bioplasma concept in the study of the biological terrain and its equilibrium. In: Proceedings of the International Symposium on Wave Therapeutics, Versailles, 1979, ed. Yan, Y., Popp, F.-A., Sigrist, S., Schlesinger, D., Dolf, A., Yan, Z., Cohen, S., Chotia, A.,(2005). Further analysis of delayed luminescence of plants. Journal of Photochemistry and Photobiology B: Biology 78, 235–244. Zheng, J., Chin, W., Khijniak, E., Khijniak Jr., E., Pollack, G.H., (2006). Surfaces and interfacial water: evidence that hydrophilic surfaces have long-range impact. Advances in Colloid and Interface Science 127 (1), 19–27. Zimmerman JW, Pennison M.J., Brezovich I., Yang C.T., Ramaker R., Absher D., Myers R.M., Kuster N., Costa F.P., Barbault A., Pasche B. (2012). Cancer cell proliferation is in-hibited by specific modulation frequencies. British Journal of Cancer. Vol.106 (2):307–13.

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Chapter 2

The field and the photon from a physical point of view 1

2

Pierre Madl and Stephane Egot-Lemaire 1

University of Salzburg, Department of Physics & Biophysics, Hellbrunnerstr. 34, A-5020 Salzburg, AUT.; 2Rose-Hulman Institute of Technology, Department of Applied Biology and Biomedical Engineering, 5500 Wabash Avenue, Terre Haute, IN, USA Abstract: In order for life scientists to better understand the relationships between cells of living organisms and electromagnetic radiation (EMR), this chapter gives some explanations about general concepts in electrodynamics. These notions encompass the physical nature of electromagnetic energy (electric and magnetic fields), its origin and its different forms and characteristics (fields, waves, photons of different frequencies) including modalities how to generate or pick up this energy via antennas or photonic detectors – with the latter being useful for detecting ultra-weak emissions of cells. The concepts of resonance and coherence are also explained, as well as how electromagnetic radiation interacts with matter. It is the aim of this section to provide concepts of understanding that assigns the wave-like property of EMR an equally important status as is already given to the particle property. Out of this equivalence, EMR-coherence and photonic coupling among biotic structures can be deduced naturally. Correspondence/Reprint request: Dr. Pierre Madl, University of Salzburg, Department of Physics & Biophysics, Hellbrunnerstr. 34 A-5020 Salzburg, AUT. E-mail: [email protected]

1. The nature of electromagnetic energy Electromagnetic fields are physical quantities, which carry energy. As can be seen in Figure 1, the electromagnetic spectrum can be characterized by its energy, expressed in [eV] or by its frequency, expressed in [Hz, s-1] reflecting the number of oscillations per second, which is also inversely proportional to its wavelength given in [m].

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Since this spectrum contains a wide range of different wavelengths, each of which exhibits different characteristics, it is divided into various domains that are named according to their dominant mode of application. Beginning with the most energetic spectral segment that purely encompasses ionising radiation, one can find among others γ-radiation and X-rays. It is predominantly utilized for diagnostic purposes or for the characterization of properties in matter. Ultraviolet (UV) radiation is less energetic and as such found at the ionization threshold (minimum energy required to induce ionization: >10 eV or 100 mT). Thus shielding of the detector should be considered to maintain proper gains in signal strength. Furthermore, to extend the lifespan of a PMT, it should never be operated at maximum potential, rather 300–400 V below this value (Swain, 2010).

Figure 4. Channel Photomultiplier. Cross-sectional view (left) and external view with and without encapsulation (right).3

A more modern design concerns the channel photomultiplier (CPM). It still preserves the advantages of the classical PMT, yet instead of the complicated dynode structure, a bent, thin semi-conductive channel acceler3 CMPs (accessed 25th April, 2012) www.perkinelmer.com/CMSResources/Images/446570DTS_PhotomultipliersMolecularDetectionAnalyticalApplicationsMedicalDiagnos tics.pdf

Detection and measurement of biogenic ultra-weak photon emission

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ates the electrons through the channel. Secondary emissions are emitted each time electrons are obstructed by the undulating geometry of the tube, resulting in the same avalanche effect as in the classical dynode design (Fraden, 2011). As depicted in Figure 4, the CPM is polled with encapsulation material and is quite rugged compared to the fragility of classical PMTs. Other advantages of CPM technology include: i) very low background noise due to different dynode design; ii) being made of a monolithic semi-conductive channel structure, there are no charge-up effects. As with PMTs, however, cooling is again unavoidable if one wants to reduce thermal emissions of the photocathode. With the absence of dynode noise, thermoelectrically cooled CMPs enable clean separation between real events created from the conversion of a photon to a photoelectron, which leads to high stability of the signal over time. However, these ruggedized detectors still do not yield the same detector efficiency as comparable PMTs. Since active (window) diameters are quite smaller than in larger PMTs, CMPs are suitable for 2D imaging. An array of several CMPs in parallel provides a 2D detector surface with a very coarse resolution. The drawback however, is evident: the reduced surface area per detector translates into a 1/d2 lower yield compared to a large 1D-PMT. Regardless of the detector employed and as shown in Figure 2, additional amplification using an electronic amplifier is necessary. Only then can the discriminator unit convert the current spikes into a computercompatible transistor-transistor-logic (TTL) signal. Since recovery times of these detectors are very fast, the number of TTL signals per given sampling interval (ranging from ms to days) corresponds to the intensity of photon emission (Yu, 2002).

4. Experimental procedures Prior to measurements and to avoid the additive effect of ambient bias, any sample (e.g. quartz-glass cuvette housing the cell suspension) should be kept in a dark chamber for at least 15 minutes. With respect to the spectral window, quartz-glass cuvettes are preferred for liquid samples over standard glass, as it allows UV radiation originating from the sample to actually reach the detector. For biological samples, it is often required to operate the measurement cycle under controlled temperature conditions. Thus, the dark chamber (as shown in Fig. 2) is fitted with temperatur sensors, a PID controller and peltier elements to enable accurate adjustment to comply with physiological constraints – usually in a range from 0 to 50 °C. Upon placing a biological specimen into the detector chamber, as conceptualized in Figure 2, two modes of operations are possible. The first

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concerns conditioning the sample with a light source prior to measurement (delayed luminescence, DL-mode), whereas the second operates without activation and aims to detect spontaneous emissions (SE-mode). Illumination in the DL-mode requires a focused light source with a spectral range covering UV, VIS and IR (e.g. xenon-lamp with a luminous flux rating in the order of 1–2 klm). A suitable optic fiber cable routs the beam of light to the sample. The optical link, as shown in Figure 2, is recommended as this cuts off specific wavelengths; e.g. above 720 and below 310 nm. In addition, the light source can be used in full spectral mode (polychromatic DL-mode) or via a monochromator to select the desired narrow spectral window (monochromatic DL-mode). Illumination with monochromatic light stimulates resonant structure only that best interact with the incident radiation and thus provide additional information with respect to the most active re-radiated spectral window. Each measuring cycle should start with an irradiating phase that lasts from 1 to several minutes. After excitation, the subsequent DL-emission are then recorded and evaluated in a time slot ranging from 0.7 to 60 seconds. For statistical purposes, every sample should be measured at least three times (Scholz et al., 1988). Calibration of the detector is crucial and can be achieved by using reference emission sources. Usually, it is sufficient to turn towards readily available 14C isotopes (β-emitters) in combination with fluorescent organic solutions that are frequently utilized in calibration procedures for scintillation counters. The isotope comes in a range from 1–2 kBq (27–54 nCi), which needs to be coupled to the fluorescing scintillation solutions consisting of 2,5-Diphenyloxazole and 1,4-bis-(2-methylstyryl)-benzene. The β-radiation from the isotope induces weak fluorescence, which is recorded by the detector. Calibration of the detector assures reproducibility and reduces measurement errors to levels of a few counts per second (Popp et al., 1984; Yu, 2002).

5. Conclusion In this chapter the focus was laid on how EMR emitted by living entities – both within cells as well as outside the organism – could play a vital role in inter- and intra-cellular communication as well as in the organization of living systems. Such ultra-weak photon emission (also known as biophotons) can be measured with highly sensitive devices called photomultipliers. This type of detector has shown to be a reliable tool for diagnostic purposes within the field of biophotonics. Yet, further research efforts and improved detector efficiencies are urgently required to achieve better signal to noise ratios and enhanced photon-conversion yield. The emerging 2nd-generation detectors will eventually make it possible to explore biophysical properties in living organisms even beyond existing limitations. This will both include


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measurements of the spectral intensities of these emissions as well as 2Ddynamics within cell cultures during growth and development or during normal metabolic activity.

References Albrecht-Buehler, G. 1991. Surface extensions of 3T3 cells towards distant infrared light sources. J Cell Biol. 114(3): 493–502. Albrecht-Buehler, G. 1992. Rudimentary form of cellular "vision". PNAS, 89(17): 8288– 8292. Becker, R.O. & Marino, A.A. 1982. Electromagnetism and Life. State University Press New York, Albany. Becker, R.O. & Seldon, G. 1985. The Body Electric. Morrow Publ., New York. Bianconi, E., Piovesan, A., Facchin, F., Beraudi, A., Casadei, R., Frabetti, F., Vitale, L., Pelleri, M.C., Tassani, S., Piva, F., Perez-Amodio, S., Strippoli, P. & Canaider, S. (2013). An estimation of the number of cells in the human body. Annals of Human Biology, 40(6): 463–471. Birnbaum, K.D. & Sanchez-Alvarado, A. 2008. Slicing across kingdoms: regeneration in plants and animals. Cells, 132(4): 697–710. Capra F. 1975. The Tao of Physics: An Exploration of the Parallels Between Modern Physics and Eastern Mysticism. Shambhala Publications, Boulder. Colli, L. & Facchini U. 1954. Light emissions by germinating plants. Il Nuovo Cimento, 12, 150–153. Colli, L., Fachini, U., Guidotti, G., Dugnani-Lonati R., Orsenigo, M. & Sommariva, O. 1955. Brief Report on: Further Measurements on the Bioluminescence of the Seedlings. Cellular and Molec. Life Sci., 11(12): 479–481. Dürr, H. P., Popp, A. F. & Schommers. 2002. What is Life: Scientific Approaches and Philosophical Positions. World Scientific, River Edge. Farhadi, A., Forsyth, C., Banan, A., Shaikh, M., Engen, P., Fields, J.Z., Keshavarzian, A. 2007. Evidence for non-chemical, non-electrical intercellular signaling in intestinal epithelial cells. Bioelectrochemistry, 71(2): 142–148. Fels, D. 2009. Cellular Communication through Light. PLoS ONE 4(4): e5086, 1-8. Fels, D. 2012. Analogy Between Quantum and Cell Relations. Axiomathes, 22 (4): 509–520. Feynman, R.P, Leighton, R.B. & Sands, M. 2010. The Feynman Lectures on Physics – Millennium Edition, Volume 1; Basic Books Publ., New York. Fraden, J. 2011. Handbook of Modern Sensors – Physics, Design and Application. 4th ed. Ch.15 – Radiation Detectors. Springer – New York. Fröhlich H. 1968. Bose condensation of strongly excited longitudinal electric modes. Physics Letters A, 26(9): 402–403. Grassa, F., Klima, H. & Kasper, S. 2004. Biophotons, microtubules and CNS: is our brain a Holographic computer? Medical Hypotheses, 62: 169–172. Gurwitsch, A.G. & Gurwitsch, L.D. 1943. Twenty Years of Mitogenetic Radiation: Emergence, Development, and Perspectives. Uspekhi Sovremennoi Biologii 16, 305–334. (English translation: 21st Century Science and Technology. Fall, 1999, 12(3): 41–53.

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Hadfield, R.H. 2009. Single-photon detectors for optical quantum information applications. Nature Photonics, 3: 696–705. Ho, M.W. 1997. Towards a Theory of the Organism. Integrative Physiological and Behavioral Science, 32(4): 343–363. Ho, M.W., 2003. The Rainbow and the Worm – The Physics of Organism, World Scientific, Singapore. Kondepudi, D.K. 1982. Sensitivity of chemical dissipative structures to external fields: Formation of propagating bands. Physica A: Statistical Mechanics and its Applications, 115(3): 552–566. Kondepudi, D.K., Prigogine, I. 1981. Sensitivity of nonequilibrium systems. Physica A: Statistical Mechanics and its Applications, 107(1): 1–24. Kobayashi, M. 2013. Highly sensitive imaging for ultra-weak photon emission from living organisms. J.o. Photochem Photobiol B. S111–1344(13), 00255-8 Madl. P., Witzany, G. 2014. How Corals coordinate and organize: an ecosystemic analysis based fractal properties. In: Biocommunication of Animals. Heidelberg: Springer, 351– 382. Musumeci, F., Godlevski, M., Popp, F.A. & Ho, M.W. 1992. Time Behavior of Delayed Luminescence in Acetabularia acetabulum. In: Popp, F.A., Li, K.H. & Gu, Q. (eds) Advances in Biophoton Research. World Scientific, Singapore. Pokorný, J., Hašek, J., Jelínek, F., Saroch, J. & Palán, B,. 2001. Electromagnetic activity of yeast cells in the M phase. Electro Magnetobiol., 20: 371–396. Pokorný, J. 2004. Excitation of vibrations in microtubules in living cells. Bioelectrochemistry 63: 321–326. Popp, F.A., Nagl, W., Li, K. H., Scholz, W., Weingartner, O. and Wolf, R. 1984, New Evidence for Coherence and DNA as Source, Cell Biophysics Vol. 6, 33–52. Popp, F.A., Li, K.H., Mei, W.P., Galle, M. & Neurohr, R. 1988. Physical aspects of biophotons. Experientia 44(7): 576–585. Popp, F.A. & Li, K.H. 1993. Hyperbolic relaxation as a sufficient condition of a fully coherent ergodic field. Int. J. Theoret. Physics, 32(9): 1573–1583. Popp, F.A 2005. Essential differences between coherent and non-coherent effects of photon emission from living organisms. In: Shen, X & vanWijk, R. (eds) Biophotonics – Optical Science and Engineering for the 21st Century. Springer, New York. Presman, A.S. 1970. Electromagnetic fields and Life. Plenum Press, New York. Reimers, J.R., McKemmish, L.K., McKenzie, R.H., Mark, A.E. & Hush, N.S. 2009. Weak, strong, and coherent regimes of Fröhlich condensation and their applications to terahertz medicine and quantum consciousness. PNAS, 106(11): 4219–4224. Rinkevich, Y., Lindau, P., Ueno, H., Longaker, M.T., & Weissman, I.L. 2011. Germ-layer and lineage-restricted stem/progenitors regenerate the mouse digit tip. Nature, 476(7361): 409–413. Roschger, P. & Klima, H. 1985. Untersuchungen von NOx-Schaedigung an Wasserlinsen mit Hilfe der ultraschwachen Photonenemisison. Atomic Institute, University of Vienna, AIAU-Report No. 85501. Rossi, C., Foletti, A., Magnani, A. & Lamponi, S. 2011. New perspectives in cell communication: Bioelectromagnetic interactions. Semin Cancer Biol. 21(3):207–214.


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Ruth, B. 1977. Experimenteller Nachweis ultraschwacher Photonenemissionen aus biologischen Systemen. Dissertation, University of Marburg. Salari, V,. Tuszynski, J., Rahnama, M., Bernroider, G. 2011. Plausibility of Quantum Coherent States in Biological Systems. JPCS, 306: 012075, 1–10. Scholz, W., Staszkiewicz, U., Popp, F. A. & Nagl, W. 1988. Light-Stimulated Ultraweak Photon Reemission of Human Amnion Cells and Wish Cells. Cell Biophysics. 13: 55–63. Schrödinger, E. 1944. What is Life? The Physical Aspect of the Living Cell. Cambridge University Press, Cambridge. Swain, J. 2010. Detectors for the quantized electromagnetic field. Summerschool on biophotonics and application of biophotons. Neuss. van Wijk, R. & Schamhart, D. 1988. Regulatory aspects of low intensity photon emission. Experientia, 44: 586–593. Yu, Y. 2002, Biophotonenemission von Gerstensamen (Hordeum vulgare L.). Dissertation at the Johannes Gutenberg University of Mainz. Zukav, G., 2007. La Danza dei Maestri Wu Li Masters. La fisica quantistica e le teorie della relatività spiegati senza l’aiuto della matematica. Corbaccio Editori, Milan.

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Chapter 4

Equilibrium and far-from equilibrium states 1

2

3

Claudio Rossi , Pierre Madl , Alberto Foletti and Chiara Mocenni

4

1 Department of Biotechnology, Chemistry and Pharmacy and Complex Systems Community – University of Siena, Italy; 2University of Salzburg, Department of Physics & Biophysics, Hellbrunnerstr. 34 A-5020 Salzburg, AUT.;3Department of Innovative Technologies University of Applied Sciences of Southern Switzerland, Switzerland; 4Department of Information Engineering and Mathematical Sciences and Center for Complex System Community, University of Siena, Italy

Abstract: This chapter emphasizes some basic aspects of entropy, enthalpy, free energy and how these interact under various conditions. We first present the classical physico-chemical point of view to promote a proper understanding of how biotic structures are seen from this perspective, which helps to comprehend the complexity of biotic response patterns. The transition from static to dynamic reactions is achieved by referring to phenomena like Bénard-Rayleigh convection cells and Belousov-Zhabotinsky reactions of abiotic systems that are complemented with biological examples. The chapter concludes by briefly focusing on the intrinsic interdependence of matter, energy and information. Correspondence/Reprint request: Dr. Claudio Rossi, Department of Biotechnology, Chemistry and Pharmacy and Center for the Study of Complex Systems – University of Siena, Italy. E-mail: [email protected]

1. Introduction With the formulation of the 2nd law of Thermodynamics (LoTD), only six years after the publication of “The Origin of Species” (Darwin, 1859), the so-called "Darwin Clausius Dilemma" (DCD) emerged as a serious challenge (Glansdorff & Prigogine, 1971). Initially, the unfolding discussion focused on biophysical aspects, which made it possible to embrace metaphysical issues (Bateson, 2000; Whitehead, 1978). For the purpose of this chapter, it is sufficient to know that the DCD addresses the issue of how an organism – that produces disorder (entropy, S) – does not choke in its own entropy. During growth and development, an organism progressively realizes higher levels

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of physical order thereby reaching a final (stationary) state of low entropy that is even maintained for extended periods of time. Initially, the outcome of this discussion documented a shift in description from physical towards energetic aspects that makes self-organizing (i.e. autopoietic) principles of living matter possible (Figure 1). Indeed, autopoiesis states that any increase in order within a system (i.e. reduction of entropy) is possible only if a high degree of internal coherence prevails (Koutroufinis, 2008). Already Whitehead (1978) stated that anti-entropic principles are deeply interwoven in the network of life in which “acts of perception” are the governing principles of emerging properties – and this ultimately must include an informational dimension as well. Understanding these properties is not as difficult as finding an exhaustive physical and mathematical formalisation. Systems are usually classified into three different types, depending on how they interact with the environment: isolated, closed and open systems. This classification is fundamental for dealing with the study of natural systems and their evolution through different states. A system state is defined by its state variables (volume, pressure, temperature, content of chemical constituents) and their functions (state functions). Examples of state functions are energy and entropy (Kondepudi & Prigogine, 1998; Prigogine & Stengers, 1979).

Figure 1. Qualitative visualization of the evolution of life. The continuous conversion and dissipation of free energy toward heat promotes equilibrium states that eventually culminate in autopoietic systems.

When its state variables remain constant over time, the system is considered to be in equilibrium with its environment. This stable state is characterised by a reversible and time-invariant behaviour. If these conditions

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are not satisfied, the system is considered to be in a non-equilibrium state. The latter can be divided into two main categories: near-equilibrium and far-from-equilibrium systems. Newtonian physics, reduces system dynamics and evolution to alterations of an ensemble of trajectories that can be calculated if its motion and the instantaneous state of the system are known. This means that any state describes the entire system, not only its future evolution but also the past, which brought the system to its present state. This behaviour reveals three fundamental characteristics of classical and quantum physical phenomena: legality, determinism and reversibility with respect to time. Obviously, this classical approach falls short in explaining the behaviour of natural systems, in which the mono-directionality of time is one fundamental intrinsic property. Moreover, living systems follow the non-linear laws of biological evolution leading to the origin of emergent properties, the real essence of life (Kondepudi & Prigogine, 1998; Prigogine & Stengers, 1979; Nicolis & Prigogine, 1977; Nicolis & Prigogine, 1989). Such emerging properties concern all the issues that hold it together and help to self-organize itself in a constantly changing environment. While a single cell is capable of doing a limited set of tasks, an ensemble (e.g. an organ like the brain) yields an emergent property (e.g. thoughts) that stretch well beyond the reach of single cells. Hence, the evolution of life displays an intrinsic anti-decaying tendency (Koutroufinis, 2008), in which living entities are not just more than the sum of their single parts, they transmutate to completely different entities revealing completely new properties (Pietschmann, 2013).

1.1. Equilibrium and far-from equilibrium states A system is in equilibrium with its environment when thermal, pressure and chemical gradients between the system and its environment are balanced. On the other hand, a stable state far-from equilibrium, is maintained by the flow of energy and/or matter to/from outside. As this system dissipates entropy, it remains in a far-from equilibrium state (Nicolis & Prigogine, 1989). Both equilibrium and close to equilibrium steady state systems are non-evolving and therefore do not depend on time. All thermodynamic descriptors, i.e. temperature, pressure, chemical potential and entropy are therefore constant.

1.2. Equilibrium and linear steady states Near-equilibrium states are predictable deterministic systems. Linear steady states near equilibrium are the result of gradients of one or more intensive properties (e.g. temperature), which create flows in extensive quantity (e.g. thermal energy). The forces acting on the flows are gradients, e.g. thermal, electric, pressure, concentration, chemical potential and chemical affinity gradients, which are correlated among each other, so much so

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that natural phenomena are subject to multiple correlated effects. In the case of steady state systems near equilibrium, these are governed by linear relations, i.e. the forces acting on the system have constant values, as well as consequent flows. Thus the resulting steady state is stable but never reaches equilibrium.

1.3. Non-linear steady states These states can be chaotic, capable of generating ordered structures and, from a biological point of view, the more interesting systems. Non-linear, far-from-equilibrium systems reveal dynamic order, are unpredictable (non deterministic), produce maximum entropy (feed-backing their order), and by fluctuation processes can pass from one steady state to another. Figure 2 refers to biotic examples that reflect such behavior. Here, various outside stressors induce a shift towards a new far-from-equilibrium steady state.

Figure 2. Multi-equilibrium view. Ecosystem shifts from a more to a less desirable state. The stability landscape depicts the basins of attraction (ball) at different conditions. Even a moderate perturbation may induce a shift into an even more stable basin of attraction (modified after Madl et al., 2005, Deutsch et al., 2003).

On a molecular level, these transitions are usually amplified and coupled with molecular motions, generating states of maximum entropy production, which in turn establish stable steady states that ultimately dissipate entropy to the environment through dynamic space-time coherent processes – examples include cell division (meiosis/mitosis) or signal transduction in neurons, etc. (see also Figure 3). Regardless of the kind of biotic


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structures these are all open systems, and as such defy mathematical characterization (Koutroufinis, 2008).

Figure 3. Idealized adaptation pattern as a result to environmental stimuli (e.g. external phosphate concentration) versus substrate availability of Anabaena sp. Upon

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reaching a set-point where the external concentration matches the uptake reaction of polyphosphate-polymerization, the expended energy for coping with the concentration gradient reaches a minimum. Once this steady state is disturbed (e.g. increase of external phosphate concentration) the organism adapts by boosting the uptake reaction kinetics. The energetic effort of Anabaena rises, pushing the organism towards a temporary entropy-maximum. (Falkner et. al., 2006; 2009 pers. comm.).

So what drives systems in their evolution towards irreversibility? Random forces omnipresent within the universe tend to push systems towards a thermodynamic equilibrium (Figure 4). However and under specific conditions, these operate to establish robust steady states, with high information contents that are far from equilibrium. Paradoxically, these forces have a common origin, namely Gibbs Free Energy. It characterizes the “useful” energetic content of a thermodynamic system to do work (under constant pressure and temperature conditions).

2. The thermodynamic branch descending towards equilibrium All spontaneous processes in Nature show a decreasing trend of “Gibbs Free Energy” (∆G) through the reduction of both physical (e.g. temperature and pressure) and chemical potentials within that system. ∆G is a thermodynamic function, which contains information about the possibility of a system to evolve. A decrease of ∆G corresponds to an increase of Total Entropy – as shown in Figure 4 – and as such indicates also the time direction of a spontaneous process. For any system near-to or far-from equilibrium, the path towards equilibrium is a natural fact. While it is evident that a decrease in energy is the driving force of the process’ spontaneity (e.g. a falling apple and its associated decrease in potential energy), it is not so obvious how irreversibility – the other guiding force – acts on entropy. A system tends to reach equilibrium by losing energy, order and information content. As shown in equation (1), the decrease of ∆G in a system may reflect either an energy decrease, or an entropy increase (increase of disorder): ∆G = ∆H - T·∆S

(1)

Where ∆H is the Enthalpy (or the system’s energetic term) and T ∆S is the entropic term (product of temperature and entropy).


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Figure 4. Increasing entropy along the arrow of time depicted in a container of gas (top) and earth’s atmosphere under the influence of gravity (bottom). While the former (under the influence of kT) starts to spread throughout the box thereby reaching a uniform state of thermal equilibrium, in the latter case gravity tends to achieve the reverse (modified after Penrose, 2004).

All spontaneous processes towards equilibrium are guided either by the energy or entropy term. Regardless, they may act independently and in opposite directions. Whenever ∆G is negative a process occurs spontaneously. In case ∆G equals zero, either equilibrium or a steady-state condition is maintained. The formation of water from hydrogen and oxygen is a spontaneous process driven by the energy term (∆H). The energy of the system decreases sharply after the reaction, while the order of the system increases – entropy reduction due to the fact that water-molecules are more ordered with respect to parental gas-molecules – so is ice more ordered than water (Sgas >> Sliquid > Ssolid). Figure 5 depicts the change in free energy when ice is fused and water vaporized – both processes are driven by the entropy term (T ∆S). Recall from Ch.2, that heat is a by-product of infrared (IR) and since that is electromagnetic in nature, water as coupled oscillators is a net IRabsorber. Thus decoupling of water-clusters at the liquid-gas interface is augmented by resonances, thereby facilitating loosening of H-bonds with the underlying water body. This process comes along with charge separation, i.e. vaporized water molecules usually carry a predominantly negative charge (Madl, et al. 2013), leaving behind positively charged bulk water.

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Figure 5. Enthalpy of Fusion and Vaporization of ice, water and vapour at room temperature. When heat is added the change in enthalpy is positive; i.e. changes from solid to liquid or liquid to gas. Below 0°C/100°C, the changes in enthalpy (∆H) and entropy (T ∆S) yield positive free energies (∆G). Fusion and vaporization are non-spontaneous processes. Because of the raised temperatures above 0°C/100°C, the combined changes in enthalpy (∆H) and entropy (T ∆S) yield negative changes in free energy (∆G). In these cases, fusion and vaporization are spontaneous (modified after Hecht, 1994).

Figure 6. Three Thermodynamic Subsystems – Sun, Biosphere and Universe. The Biosphere extracts negative entropy in the process of exchanging "visible" photons (black body radiation at T= 5800 K) to "invisible" photons (Black Body radiation at T = 280 K). While the total energy is the same, the visible photons have less degrees of freedom (DoFs) than their invisible counterparts, which also implies that the former is less entropic than the latter. This process accounts for the mysterious "life force" that seems to defy the 2nd Law of Thermodynamics.

It is this positive charge that is thought to act as the promoter for icelike lattice formation prior to freezing (Pollack, 2013). Considering that IRdriven evaporation extracts energy from the liquid phase with each molecular cluster that leaves the matrix, it would be possible to obtain a super-


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cooled liquid – indeed this is a trick that is used in laser cooling (Zhang et al., 2013). With this in mind, another reference to Ch.2 is worthwhile. Earth as a dissipative system utilizes these principles to establish order in that it receives high-energy solar quanta and reradiates an even larger amount of low-energy quanta back to space. Energetically, the match is only possible if the higher number of IR-quanta released into space equals those fewer incoming UV-VIS quanta (Figure 6). This numerical imbalance assigns incoming radiation fewer degrees of freedom (DoF) than the outgoing counterpart, or in other words: solar radiation is low-entropic, whereas terrestrial radiation (IR) radiation is high-entropic (recall that entropy is a measure of disorder, homogenization or lack of differentiation). Those fewer DoFs are crucial to delimitate a smaller phase-space region that translates into smaller entropy when compared to reradiated quanta. Photosynthesizing organisms take advantage of this low-DoF-radiation by converting it into biomolecular structures with likewise low DoFs thereby reducing their own entropy. In turn, secondary trophic layers feeding on these low-entropic primary produce to generate their own low-entropic matrices (Penrose 2004).

3. Steady state systems: order from disorder Energy reduction and entropy increase act independently in determining the evolution of spontaneous processes towards equilibrium. Sand grains placed on a resting plate for example are in a stable state, with each grain having the same potential energy. Geometric pattern formation however can be induced via an influx of energy, i.e. oscillating modes of resonances. Doing so modifies not their energy but the overall configuration to form a macroscopic “coherent” pattern (Figure 7). It is only after the action of chaotic forces that decoherence and disorder take over. Chaotic forces are ubiquitously at work in any system and again act at all levels (See Figure 1). At the molecular level for example, diffusive forces can be considered isotropic and chaotic in origin. Such vibrations modify or even degrade ordered structures at a macroscopic level. In essence, isotropic fluctuations are the origin of entropy changes and alterations of order. Here too, one finds concept of the “arrow of time” as deduced from the DCD or shown in Figure 4 and which was finally formalized by Prigogine (1991). Non-equilibrium systems can be grouped into steady state systems near equilibrium (linear steady states) or steady states far from equilibrium (nonlinear steady states). This is not to be confused with biological complexity as this is neither ordered nor disordered (Figure 8). The ordered pathways in which metabolic processes take place is found in the delicate steady-state balance of order and chaos, as can be seen in homeostasis.

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Figure 7. Chladni’s vibrating resonance patterns using sand grains (Tyndall, 1869). These plates illustrate how ordered states of equilibria can arise from disorder. Pattern formations are the result of different resonance frequencies. Dark areas denote regions of intense vibrations (high isotropic fluctuations), which are bound by bright lines with low isotropic fluctuations (nodal lines of zero vibration).

Figure 8. Complexity is found between the perfect arrangement of a crystal (order, reduced to a low-entropic microstate with a single DoF) and the chaotic patterns of molecular diffusion in a gas (disorder, yielding high-entropic, maximal microstates and infinite DoFs).


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4. Nonlinear regime: far-from-equilibrium systems When flows are no longer linear functions of forces, a system can reach a far-from-equilibrium state, which can still evolve towards various stationary states that differ from each other. In such a system, the growth of fluctuation drives it out of the linear regime and further into instability. Once the distance from equilibrium exceeds a threshold-level of stability, it evolves towards organized non-equilibrium states, so-called dissipative structures.

4.1. The concept of dissipative structures The fundamental difference between linear and nonlinear systems resides in the nature of the process, which describes the change of internal variables in relation to the magnitude of the perturbation. The figures 9.a,b both reveal linear and non-linear behaviour of a general variable. The concept of stability loss for a system governed by nonlinear differential equations can be expressed as follows: dx = − x3 + r ⋅ x dt

(2)

where x is a variable related to the state of the system and r a parameter associated to the force acting within the system. The main goal here is to find stationary solutions x as a function of r. Figure 9.a shows a graph, which reports x as a function of r (0 < r < 3). The three real solutions of a stationary state depend on the r-value: when r < 0 there is only one solution (thermodynamic balance). Symmetry is broken once r = 0 (bifurcation point), where two different solutions become stable: x = + x and x = − x . Thermodynamically, the system is stable when a state is able to quench small fluctuations. However, when the fluctuation increases further, it becomes unstable thereby pushing it away from this stationary state, forming dissipative structures. Although both these states have the same probability, it is impossible to determine a priori which one the system will choose. The behaviour of far-from-equilibrium states are not deterministic and thus unpredictable. Nature shows many evidences of asymmetrical behaviour, such as the preferred natural synthesis of bio-molecules like L-amino-acids, Dribose in DNA and RNA or polyphosphate-polymerization as shown in Figure 3). Yet the difference between abiotic and biotic structures is rooted in the fact that the latter make use of “quorum sensing”. This principle enables cell-populations to coordinate gene-activity e.g. certain proteins that act as quorum sensing factors (Sang-Wook et al., 2011). If under a given environmental stimulus, one population of proteins is favoured over the other, the bifurcation (as shown in Figure 9.a) may take the upper pathway (e.g. when

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past experiences entitles a cell to “recall” response options to better cope with the stimulus and come up with a corresponding protein concentration), however at other times different environmental stimuli may force the system to flip to the lower pathway (e.g. when an intense stressor induces a shock-stimulus and triggers the collapse of the responsive protein synthesis). In either case, the principle of bifurcation enables a biotic system to adjust to various external stimuli. As a result, time evolution of far-fromequilibrium systems can only be determined experimentally. Dissipative structures are well known and range from basic biochemical oscillations to the cardiac rhythm and other chronobiologic patterns. With reference to evolutionary processes, biological systems share peculiar steady state properties, like multi-stability and time dependency. Depending on the initial conditions, the system will always tend towards a minimum of entropy. However, fluctuations between one state and another are possible because of system oscillations; e.g. the oscillations of cyclic AMP (cAMP) in Dictyostelium cells (Goldbeter, 2007). Similarly, heart rate variability reflects the robustness (quenching) of the system, which is comparable to the overall state of health (Thayer et al., 2012). Such states are oscillatory pathways that are described by a limit cycle in phase space (e.g. Lorenzattractor). These systems become completely unpredictable and the processes as such are stochastic. As illustrated in Figure 9.a, which reports the bifurcation diagram of the discrete time Logistic map xt+1 = r xt(1-xt), the equilibrium is unique in the region of r in-between 1 to 3. The bifurcation point is denoted for r = 3. For r’s > 3, subsequent bifurcations lead to successive alternations of 4, 8, 16, 32 values and so on. When exceeding the critical value of r = 3.570 the system becomes unstable and unpredictable (“deterministic chaos”).

Figure 9.a: The Logistic Map, that when rotated by 90o, in principle overlaps and corresponds with the pattern as shown in Fig. 9(b).


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In living systems, the coupling of chemical or biological kinetics and diffusion give rise to ordered, compartmented spatial structures. These are spatial ambits in which entropy undergoes a sharp reduction because it is dissipated into the wider environment. The amount of entropy dispersed is greater than that produced by the system. In this way, the process is irreversible and spontaneous. Biotic systems pass from conditions of minimum entropy production to conditions of maximum entropy production (see again Figure 3), in which high dissipation creates and maintains system order.

Figure 9.b: Waddington’s epigenetic landscape in a simplified illustration of cancer formation (modified after Gryder et al., 2013: Waddington, 1957).

Waddington’s epigenetic landscape (Figure 9.b) fits perfectly into this framework as differentiation from a single totipotent cell (zygote with a large number of genes active (high degree of demethylation, hence being very entropic), to trillions of differentiated somatic cells (with the majority of genes deactivated, thus suggesting a lower state of entropy) that constitute an adult organism (Fulka et al., 2004; Reik et al., 2001). The negentropic principle of the epigenome becomes only evident when considering that gene de/activation is governed by reducing individual DoFs at cellular level in favour of a common goal at the organismic level. Entropy however does increase, when long-lasting stress events lead to increments of cellular DoFs and ultimately to chronic disease processes, e.g. such as induced dedifferentiation towards precancerogenous cells. Here, growth and development as well as environmental influences act on gene activity, thereby silencing or activating specific genes, which ultimately will be reflected in altered bifurcation patterns. Although the influence on gene expressivity is per se a

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necessity for proper cellular functioning, synergistic effects can turn a normal response pattern into undesired pathways of chronic disorders (Gryder et al., 2007). Dedifferentiation of unipotent cells is a characteristic of tumorgenesis, which leads some scientists to even speak of “reversing the onthological principle back to a more fetal-like state” (Holtan et al., 2009), or in simple words: dedifferentiation renders somatic cells again more pluropotent. Thus a tumor can be considered an attempt of some cells to turn the ontogenetic clock backwards thereby increasing DoFs on a cellular level and ultimately becoming again more entropic.

4.2. Simple dissipative structures The basics of this concept are well illustrated by low-level self-organized systems, like the formation of Bénard cells (Prigogine, 1991). By heating a liquid contained between two thermal conductors at different temperatures the flow of energy follows a kinetic path driven by Brownian motion. At a critical thermal gradient the system spontaneously self-organises. The motions are not longer Brownian, but convective (Figure 10.a). Cylindrical or hexagonal columns form, in which hot and cold liquid flow cyclically. This abiotic system remains stable as long as the boundary conditions are maintained.

Figure 10.a: A phase transition is induced when a certain energetic threshold level is exceeded. Only at that point can Bénard-Rayleigh convection cells spontaneously become manifest, where cyclic turnover of low-density, lighter and warmer bottom-water of a pan raises to the top while cooler and denser top-water sinks back to the bottom. Top: lateral view, bottom: top view.


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Although it is currently impossible to determine the trigger, or establish where in the system the process arises, its beauty, stability and potentiality to evolve is lively testified within the biosphere. A magnificent organismic manifestation can be found in archaic invertebrates, within the phylum cnidaria - commonly known as corals (Figure 10.b). Although corallite development is rather complex, the modular morphology of siderastriids share some essential features of Bénard cells in that they tie together dissipative structures and the associated flow of energy to yield distinctive morphological phenotypes. This similarity with Bénard-Rayleigh-like celltype, along with re-organization giving rise to a honeycomb-like appearance is not accidental at all even though it is biochemically rather than thermically driven.

Figure 10.b: The cerioid arrangement of Siderastrea savignyana1 nicely illustrates the Bénard-Rayleigh-like analogy. Each corallite is made of septa and disseptiments that by themselves assure regularity of this cyclic pattern.

4.3. Complex dissipative structures Chemical reactions governed by nonlinear kinetics can produce spatiotemporal organized phenomena, including periodic and chaotic concentrations dynamics, travelling waves, and stationary spatial patterns (Epstein & Poiman, 1998). When driven out of equilibrium, these mechanisms often exhibit spontaneous phenomena of symmetry breaking and altered spatial patterns. In Turing models with two-state-variables (activator and inhibitor) and in absence of diffusion, the concentration of the chemical species tends towards a linearly stable uniform steady state (Turing, 1952; Castets et al. 1990). However, spatially inhomogeneous patterns can emerge once the diffusion coefficient of the activator is much smaller than the diffusion coefficient of the inhibitor. Close to instability, such systems are particularly sensitive to exter1

http://coral.aims.gov.au/factsheet.jsp?speciesCode=0485

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nal stimuli, so the presence of periodic or noisy (even quantum) signals can set-off an oscillating behaviour. Oscillating reactions like the Belousov-Zhabotinsky (BZR), (Belousov, 1958; Zhabotinsky, 1964; Winfree, 1972; Facchini et al., 2009) are classical examples as these yield ordered spatial dissipative structures (Epstein & Poiman, 1998) that can attain many patterns (Nicolis & Prigogine, 1977; Prigogine, 1991; Prigogine, 1980; Peacocke, 1983; Vitagliano, 1990). The BZR – initially proposed as a simplified scheme of a metabolic pathway (e.g. Krebs cycle) – is a catalytic oxidation-reaction that was adopted as a prototypical model of nonlinear phenomena and pattern formation.

Figure 11.a: Example of Turing structures in the Brusselator system. Initial, chaotic situation (left) and onset of stable pattern formation once the system’s threshold kinetics is exceeded (right).

Figure 11.b: Similarity between the meandroid pattern of the brain coral Platygyra lamellina2 (left) versus BZR in vitro (right).3

2 3

http://photos.foter.com/67/brain-coral.jpg http://vironevaeh.files.wordpress.com/2012/11/photo1.jpg


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The BZR can be modelled by the Brusselator (DeWitt, 1972), which describes the spatio-temporal evolution of two chemical species subject to both reaction and diffusion mechanisms. The interesting behaviour of this model is that there are ranges of kinetic rates and critical values of diffusion coefficients for which the asymptotic steady state is stable in time, but unstable in space, yielding various spatial patterns; e.g. spot patterns as shown in Figure 11.a. Such reaction-diffusion mechanisms, specifically autocatalytic and oscillatory reactions, also describe self-organizing behaviours, such as propagating fronts, duplicating bacterial colonies, advancing regions of metal corrosion, or infectious diseases spreading through populations. As indicated by Figure 11.b, the biochemical oscillations within another group of scleractinian corals enable them to shape morphologies that differ significantly from the Bénard-Rayleigh-like arrangement. Here the very thin film of living tissue covering the coral skeleton (usually only few millimetres thick) reveals BZR-like reaction patterns during precipitation-reactions of the underlying skeletal aragonite-matrix that ultimately results in their distinctive phenotype. One of the most important mechanisms determining spontaneous spatio-temporal dynamics, self-organization and symmetry-breaking mechanisms of biological cells is chemotaxis. Coordinated motion of cells is the result of different paths of cell-to-cell signalling. Intercellular communication that governs the transition from isolated to collective phases of life can be demonstrated by the slime molds Dictyostelium discoideum or Physarum polycephalum. These cells periodically emit a chemical signal to attract neighbouring cells. This chemotactic response explains the wavelike aggregation often observed in-vitro (Goldbeter, 1996).4

Figure 12. BRZ-like fluorescence pattern in Stenopus hispidus used in interspecific communication (Madl & Witzany, 2014).

4 Mycomycete P.polycephalum in action (accessed Aug. 2013): http://www.youtube.com/watch?v=5UfMU9TsoEM

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In general, spatial symmetry breaking aids to differentiate between embryology and morphogenesis. It differs from spatio-temporal pattern phenomena generated by waves, in the sense that these vary with time. Morphogenetic mechanisms can generate steady states by revealing spatially inhomogeneous patterns, as observed in fur-patterns of zebras, leopards (Murray, 2002) or many tropical species of fish and invertebrates (Figure 12).

5. Informational networks in dissipative systems Organisms are primarily entities embedded in an informational network as constituted by the biosphere. Energy flows assure nutritional requirements, and informational “hand-shake” patterns from one organism to the next and become manifest via phenotype-environment coupling; i.e. organisms not only acquire given phenotypes, they literally feed back and shape their environment. However, such interaction implies irreversibility in that the entire ecosystem reverberates due to the interaction of each member organism. The basic reasons of irreversibility are dramatically simple: systems evolve towards lower energy and/or lower information states; in the case of senescence; decomposition of organic matter reduces both the energy of the system as well as its information content. Hence it is not surprising that living matter requires a complex network of interaction- and communicationpathways to maintain their low-entropic state and to stabilize structure as well as coherence (see Figure 3). While chemical signalling has been profoundly investigated with many chemical mechanisms of cell-cell interactions, well physically mediated interactions – especially those supported by electromagnetic (EM) signalling – are still poorly understood but essential to understand this complexity. Basically, three main processes may be used to comprehend electromagnetic radiation in cell signalling: i) cellular responses to externally generated EMperturbations, ii) detection of EM-radiation generated by cells and organisms, iii) detection of cell-cell perturbation by non chemical signalling (Cifra et al., 2010; 2011; Rossi & Foletti, 2011; Bolterauer et al., 1991; Vos et al., 1993; Jelinek et al., 2009; McCaig et al., 2009). While there are few papers dealing with IR emission (Fraser & Frey, 1968), several report evidences of UV-VIS-radiation emitted by cellular systems. This property was either found to be correlated to a specific phase of cellular metabolism or associated to cellular systems that undergo physico-chemical stress exposure (Slawinski, 2003, 2005). The mechanism of EM-generation is theoretically deduced both at molecular and cellular levels (Pokorny et al., 2005). Microtubules, usually generate strong electric dipoles (>1000 Debye). Such dipoles coupled to metabolic energy do satisfy the postulated presence of coherent fields in the cell (Fröhlich, 1970; 1975; 1988, Fröhlich & Kremer, 1983). The


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continuous alternation between growth and depolymerisation of the cytoskeleton provides a dynamical condition that, coupled to the electric dipoles could determine a mechanism of EM-energy dissipation both in the IR-range as well as in the optical region. Other sources of strong static electric fields are mitochondria, cell membranes and other organelles. Coupling between dipolar, subcellular structures brought into an excited resonant state (induced by cell metabolism) along with the dampening effect induced by the cytosol, can likewise induce EM-emissions (Pokorny, 2003). Generation of electro-solitons within the biological systems are also known to generate even microwave emissions (Brizhik et al., 1989; Brizhik & Eremko, 2003). Whatever the underlying mechanism of EM-generation may be its purpose is still debated (Popp, 1992; Fels, 2009; Scholkman et al., 2013). So far, this question is only partially answered – open issues remain and regard the mechanisms through which complexity emerges from chaos and how equilibrium systems are stabilized by informative processes. One approach that helps to shed light on this matter concerns the equatability of negentropy as it is directly involved in the creation of information. With reference to Figure 1, the evolution of complexity – or the "essence of life" – is linked with the growing role of information (I). Since transformations of matter within biotic systems do not occur randomly, rather the mediations of signs & signals need to be coupled to matter and energy. While free energy (E) is codified by vibrations, matter (m) in this respect can only be envisioned in its condensed form. As depicted in Figure 13.a, the entity that bridges energy and matter relates to the purposeful coupling via information-based catalytic activities. Inevitably, this leads to a systemtheoretical definition of information, which can be subsummized as the »bit« of information constituting the difference that makes a difference (Bateson, 2000). Since energy can neither be created nor destroyed, all forms of energy must be transformed into a different level of quality. Thus the variations of the sum giving the total energy, approaches zero: i.e. dE + dm + dI = 0 .

(3)

This formula has fundamental implications in that the gradual evolution of a complex system causes an increase in information (dI), which is compensated by a concomitant decrease in the flows of energy (-dE) and/or matter (-dm). Seen from a conventional perspective, this qualitative transgression to higher states is reflected by the gradient of Negentropy and Entropy (N/S); i.e., “heat” belongs to a “low quality” kind of energy and is expressed as N/S < 1, while information is grouped as a “high-quality” kind of energy and is denoted as N/S > 1 (Figure 13.c).

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Figure 13. The EmI-Triad (a) and its potential role to comprehend the phenomenon of life. The energy aspects representing "the potential for causing change", is a concept used to understand the dynamics of most physico-chemical processes. The Fertile Evolution Principle (b) depicts how the use of free energy bridges soma (matter) with significance as perceived information cycling through the system pushes it into a more evolved configuration. Order and disorder in biological systems (c) are expressed as ratios of N/S (adapted from Madl & Yip, 2007; Manzelli, 2007).

Hence, the Fertile Evolution Principle (FEP) can be seen as a function of the “Evolution of the Quality of Energy”, driving all steps of autopoiesis (Manzelli, 2007). To avoid chaos, the triadic categories of cyclic interactions imply that cyclic codification (using “I” to convert “E” into “m”) assures that free vibrational energy is entrapped and stabilized in complex bonding structures through catalytic activities. Cyclic decodification (converting “m” into “I” – Figure 13.b) must then occur by breaking the particle-wave duality, thereby setting off a pure wave (information energy “I”). In order to complete the cycle, the transformation from information to energy (conversion of “I” to “E”) involves entanglement between a wave and a particle, producing photons and phonons (recall Ch.3). Such quantum-coupled waves, or "pilot waves" are portions of energy related to three-dimensional manifestations – photons, phonons and matter (e.g. electrons, atoms and molecules, etc.). Experimental abiotic evidence confirms that pilot waves are not at all virtual concepts, but responsible for real physical effects (Bush, 2010; Molácek & Bush, 2013; WindWillassen et al., 2013). Moreover, a quantum information probability can be deduced that, based on instantaneous signals, induces coherence in diffractive patterns of particle motions (Harris et al., 2013, Harris & Bush, 2013). Thus, the pilot wave should be envisioned as an effective wave of information that is functionally related to self-organization of dynamic properties of particles in motion. Consequently, we must assume that this wave is not only a "probability wave-function", but in essence the kind of "information-energy" able to synchronize a particle's motion by means of oscillatory signal fluxes. As this generalized assumption is mediated by different strategies of semiotic interactions working at different levels of complexity, such "information-energy" exchanges are universal, thus must be prevalent in all evolutionary processes.


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7. Concluding remarks Biological systems are open systems that evolve according to nonlinear mechanisms. Nonlinearity is essentially related to the presence of auto-organized and unpredictable behaviour of cells in biological and biochemical networks. Indeed, nonlinearity assures coexistence of activation and inhibition phenomena without which any explanation of living systems as dissipative structures would hardly be possible. From a classical point of view issues like understanding cellular behaviour inducing long distance signalling such as synchronization in brain waves and the activation of immune responses will remain obscure. Introducing however, a concept like the fertile evolution principle based on the triadic interrelationship of energy-matter-information, the continuous emergence of information as a result of increasing system complexity opens up new possibilities to comprehend biochemical transformations.

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Chapter 5

The origin and the special role of coherent water in living systems 1, #

2

3

Emilio Del Giudice , Vladimir Voeikov , Alberto Tedeschi and 4 Giuseppe Vitiello 1

Retired Physicist, Via Friuli, 21, 20135 Milano, Italy; 2Lomonosov Moscow State University Faculty of Biology, Vorob’evy Gory, Moscow, 119995, Russia; 3WHITE Holographic Bioresonance, Milano, Italy; 4Physics Department and INFN, University of Salerno 84100 Salerno, Italy Abstract: According to quantum electrodynamics (QED) liquid water is a twophase system in which one of the phases is in a coherent state where all molecules are phase correlated, whereas the other is made up of uncorrelated molecules in a gas-like state. Recent data demonstrate that interfacial water adjacent to hydrophilic surfaces exhibits peculiar anomalous properties, e.g., it is electrically charged and the sign of its charge is the same as the charge of the contiguous hydrophilic surface. It is known as “Exclusion Zone water” (EZwater) because it excludes solutes. In this paper we show that these properties of interfacial water can be derived from the properties of coherent water. We analyze in the QED frame the dynamics of formation of EZ-water, the origin of its anomalous properties, and its relevance in living systems, where water is almost entirely interfacial being close almost everywhere to some macromolecular backbone or to some surface. We conclude that all the above properties of EZ water are the consequence of the coherent collective oscillations occurring within liquid water and on its boundaries.

#Deceased

January 31, 2014

Correspondence/Reprint request: Dr. Vladimir Voeikov, Lomonosov Moscow State University, Faculty of Biology Vorob’evy Gory, Moscow, 119995, Russia. E-mail: [email protected]

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Introduction Water in living systems has been traditionally considered a solvent in which biochemical reactions occur and which coincides with ordinary water. This vision meets some inconsistencies. We will show in the following that molecules, in particular, water molecules, don’t play a chemical role only but also a physical one, so that the role of water becomes extremely important when the physical dynamics of molecules is taken into account, suggesting that water properties in living systems differ from those of ordinary “bulk water”. In fact, in biological systems chemical reactions cannot occur at random but should follow specific well-defined sequences, where each reaction occurs at a definite site and definite time; in other words chemical reactions should follow strict space-time codes (Del Giudice et al., 1985; 1986; Barbieri, 2002). This point has been stressed a long time ago by such prominent biologists as Albert Szent-Gyorgyi (Szent-Gyorgyi, 1960) and Gerald Edelman (Edelman, 1984), who pointed out to the inconsistency of the observed biological order with a biochemistry governed by a diffusion regime. As emphasized by Schrödinger: ''it needs no poetical imagination but only clear and sober scientific reflection to recognize that we are here obviously faced with events whose regular and lawful unfolding is guided by a ''mechanism'' entirely different from the ''probability mechanism'' of physics'' (Schrödinger, 1944, p.79). A physical agent is needed to organize the traffic and collective interaction of molecules; this is not a speculation but a logical, necessary consequence of the existence of order in biological systems. In our study it emerges as a logical consequence of Quantum Electrodynamics (QED). In this connection we recall that in electrodynamics two molecules oscillating at frequencies ν1 and ν2 respectively, within an extended electromagnetic field (EMF) oscillating at a frequency ν0, develop a very strong attraction whose range could reach the size of extent of the EMF when the three frequencies ν0, ν1, and ν2 coincide, namely their differences are smaller than the thermal noise kT, which at room temperature is 0.025 eV (Askaryan, 1962; Del Giudice et al., 1986; Beige et al., 2005). Since in living organisms water appears as a medium in which biomolecules are suspended, a selfgoverning scheme for biochemical reactions emerges should water be able to give rise to an extended EMF. If water molecules are assumed to be bound by short-range static forces, (Franks, 1972-1982), they may at most oscillate in a random fashion due to thermal excitation. This excludes any possibility of a large scale coherent oscillation which, as we will see below, is of crucial importance for the water molecular dynamics. In the last decades a different picture has emerged from quantum electrodynamics (QED). This approach has been extensively described by Preparata (Preparata, 1995). Experimental investigation of water close to

The origin and the special role of coherent water in living systems

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hydrophilic surfaces (interfacial water), performed mostly in Gerald Pollack’s laboratory, has shown the existence of layers, as thick as some hundreds (up to 500) of microns, of a variety of water organized differently than usual bulk water (Zheng et al, 2006), termed “Exclusion Zone water” (EZwater). Observed properties of EZ-water imply that it represents an ensemble of molecules correlated in space and time by its own dynamics, different from the one governing the usual bulk water. Our discussion combines the theoretical QED description of liquid water with the experimental data on interfacial water and discuss its role in living systems, where the extremely developed hydrophilic surfaces, which are made up by biopolymers and supramolecular structures, transform a significant part of biological water into EZ-water.

1. What is coherence? In 1916 Nernst (Nernst, 1916) proposed that quantum fluctuations of elementary components of a physical system could be tuned together giving rise to a collective, in phase, oscillation so that the many elementary components behave in unison, as a whole, and lose their individuality. The dynamical regime corresponding to this possibility is termed coherence and is indeed experimentally observed in superfluid Helium, crystals, magnets, superconductors and other systems where ordered patterns appear. In all these systems, coherence is the macroscopic manifestation of the microscopic dynamics of the elementary constituents provided that some boundary conditions are satisfied, such as the density being above a critical value and the system temperature being below some critical threshold TC. Notice that TC does not need to be very low, as it happens in superfluids and superconductors; crystals and magnets exist at room temperature and their critical temperature TC may have quite high values depending on the specific material they are made of (Blasone et al., 2011) (e.g. the diamond crystal loses its coherence (it melts) at a temperature of about +3545 °C). The energy of the coherent state, namely the one where the system components have a well-defined common phase of oscillation, is lower than the original non-coherent ensemble of components (Fig. 1). This difference of energy is termed energy gap. The existence of the energy gap accounts for the stability of the coherent state (its robustness) against thermal or other external perturbations and prevents the individual components to transform independently of the ensemble. Below TC, the energy supplied by thermal fluctuations is less than the energy gap and therefore unable to destroy the coherence.

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Figure 1. Emergence of coherence. An ensemble of quantum particles, here water, is taken as an example. (A) Water molecules in vapor oscillate independently from each other in a non-coherent way because of long distances between them (density is below the critical density). (B) When vapor condenses into water (temperature decreases below a threshold and density increases above a threshold), water molecules start to oscillate in phase (minimum of energy) – the condition for coherence. (C) Coherently oscillating water molecules get together with associated EMFs in Coherent Domains (CDs) immersed in dense gas-like non-coherent water. The ratio of non-coherent to coherent (CDs) water in liquid water depends upon temperature.


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The dynamical regime where coherence is established does not require, in general, the presence of an externally supplied cavity and of a pump (as on the contrary it happens in laser physics). The electromagnetic fields (EMF) and the electromagnetic potential play a relevant role in the physics of coherent systems. Consider an ensemble of a large number N of quantum particles (e.g. electric charges, dipoles, multipoles). Quantum fluctuations among their inner states, e.g. their lowest energy state and one of their excited states, imply that they emit or absorb EMF radiation (radiative EMF). One can show (Preparata, 1995; Arani et al, 1995; Bono et al, 2012; Del Giudice et al, 1988; Del Giudice & Vitiello, 2006; Blasone et al, 2011) that the N particle ensemble gets coupled to the radiative EMF and transits from the non-coherent state to a (lower energy) coherent one, provided that the ensemble is above a density threshold and below a temperature threshold: the particles (the matter field) and their radiative EMF get coupled together in a coherent whole, a common in phase dynamical oscillation, in a way that the self-trapping of the original radiative EMF is generated, which thus cannot be any longer irradiated outwards (Preparata, 1995)). The in phase fluctuations of the EMF field (the gauge field) and of the matter field (the particles) characterize the minimum energy state of the system, namely the quantum vacuum. Self-trapping involves fields only. Electromagnetic potentials are not trapped. Potentials are coupled to the phase, and can give rise to observable physical consequences also in the absence of fields, provided that some topological singularities are present. This is widely confirmed by experimental observations of so called topologically non-trivial extended objects, such as, e.g., vortices (Blasone et al, 2011), and in the celebrated Bohm-Aharonov effect (Aharonov & Bohm, 1959, 1961), on which we do not comment further for brevity. As mentioned above, thermal perturbations may destroy coherence. Labor-atory observations of superfluids, superconductors and other coherently ordered materials show that at the system boundaries and/or between ordered and non-ordered domains internal to the system, a dynamical equilibrium is reached where component molecules are crossing over continu-ously between coherence and non-coherence. At any definite value of the temperature T the fraction of molecules belonging in the average to the coherent state is well defined. However it is impossible to tell which molecules belong to which fraction. When coherence is lost, the trapped EMF is released outwards in many ways. If decoherence occurs slowly the EMF energy can be released as heat (thermal radiation). When on the contrary the process is fast, as it occurs, e.g., in sonoluminescence (Putterman & Weniger, 2000), the em field is observed as a flow of photons. This is presumably the origin of the so called “degradative mitogenetic radiation” discovered by A.G. Gurwitsch (Gur-

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witsch & Gurwitsch, 1943) and of the more general phenomenon of “emission of biophotons” discovered by F.A.Popp (Popp, 1979), which are therefore the debris of a previous coherent field governing the biological system. Within coherent physical systems, including presumably biological systems, the EM field, being coherent, cannot be conceived as an ensemble of a defined number of photons; it acquires this form only after the disappearance of coherence.

2. The QED picture of aqueous systems The QED study of liquid water shows (Preparata, 1995, Arani et al, 1995; Bono et al, 2012) that a coherent oscillation of the electron clouds of water molecules occurs. Many physical observables of liquid water have been calculated in QED without any ad hoc hypothesis or modelling, such as the critical density, the specific heat, the latent heat of boiling (Arani et al, 1995). We emphasize that coherence of the electron cloud oscillation in water is not a speculation but the result of QED computations applied to liquid water. In such a coherent dynamics water molecules oscillate between the ground state of their electron cloud and an excited state at 12.06 eV; in the ground state all electrons are tightly bound and the ionization potential is 12.60 eV. In the excited state one electron becomes quasi-free and could be released away either by a quantum tunnelling effect or by a mild external perturbation. Coherence extends over the spatial region of the size of the wavelength of the EM mode of 12.06 eV, namely 0.1 microns (Preparata, 1995) (Fig. 1). Such a region is named the Coherent Domain (CD). The energy gap matches the cohesion energy of liquid water, so that this coherent process describes the transition from vapor to liquid. The frequency of the trapped EMF lies in the infrared (IR) region of the spectrum. The oscillation of the electron clouds of water molecules participating in the coherent dynamics pushes one electron per molecule just below the ionization threshold. Therefore CDs become pools of quasi-free electrons. The presence of a large number of quasi-free electrons in the CDs is the first step of the process allowing coherent water to become an electron donor, this process implying the additional dynamics described in the following. We remark that the QED results illustrated above fit completely with known facts such as the availability of electrons supplying the redox chemical reactions involved in cell respiration (Voeikov & Del Giudice, 2009). This availability cannot be explained in the picture where electrons are tightly bound to their parent molecules. In addition to the above process, another coherent process had been recognized in 1988 (Del Giudice et al, 1988). In this case the coherent oscillation involves the electric dipoles of water molecules, which oscillate between


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the molecule ground state and the low-lying rotational states at about 20 cm-1 (the cm-1 is a unit used by spectroscopists which corresponds to 30 GHz). The dipole coherent oscillation predicts the appearance of an extended fluctuating electric polarization field, which, in the absence of an externally applied electric field, averages to zero. When an external electric field E is present, the polarization field acquires a permanent component parallel to E; the size of the coherence domain relative to this process, measured by the wavelength of the EM mode responsible for the oscillation, reaches some hundreds of microns, depending on the value of E. Coherence of electric dipoles allows us to predict that liquid water close to sources of electric fields, such as polyelectrolytes or hydrophilic surfaces must be strongly polarized, which is well known from experiments (Celaschi & Mascarenhas, 1977). In bulk water – far from surfaces, molecular backbones or other sources of electric fields – rotational coherence is absent. Close to surfaces or other electrically charged bodies, the combination of the above two kinds of coherence produces most of the peculiar landscape of EZ-water, in particular the depth of the EZ layer whose size (hundreds of microns) coincides with the size of the domains of this electric dipole coherence. The complete interpretation of EZ water demands still some additional coherent processes. Ensembles of charged particles can become also coherent. Indeed, ensembles of ions suspended in water have been shown to become coherent producing an energy gap of about 3 eV per ion (Del Giudice et al., 2000). When the binding energy among ions constituting a molecule becomes smaller than the sum of the energy gaps of the components ions, the molecule splits as it occurs in the Arrhenius spontaneous electrolyte dissociation. In the case of “normal” water the binding energy of the pair H+-OH- in the individual water molecule is large enough to prevent the split (water dissociation). However, in the coherent state the binding energy of the pair decreases because the electron cloud is made wider by the coherent oscillation. This decrease is still larger when the two kinds of coherence (the electronic one and the dipole one) described above are simultaneously present. Because of this decrease the binding energy becomes quite close to the gain of energy produced by the split, which therefore can occur under small perturbations giving rise to the pH phenomenon. In any case we have a number of quasi-free protons, which mirror the existence of quasi-free electrons and can give rise to a plasma oscillation. We have seen that in liquid water many coherent processes can occur simultaneously, especially when non-aqueous impurities are present. This is the case of biological water which is a much more complex matter than pure liquid water. The major feature of biological water is that a very essential part of it is contiguous to solid surfaces of hydrophilic polymers and of their supramolecular complexes. Water adjacent to hydrophilic surfaces is highly organized and has a lot of peculiar properties that makes it very different

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from bulk water. We describe below some of the properties of this water and point out that its origin and properties cannot be explained unless one considers the coherent property of water that follows from the quantum approach.

3. Major properties of EZ-water In the last years Gerald Pollack and his associates have shown in a series of experiments that water adjacent to hydrophilic surfaces (interfacial water) is a new allotropic form of water dynamically different from bulk liquid water (Zheng & Pollack, 2003). They found that colloid particles and different solutes are excluded from aqueous zones closely adjoining to hydrophilic surfaces on the order of tens and hundreds of microns. Such water was termed exclusion zone water (EZ-water). EZ-water is different from bulk water not only in its solvent properties; it has higher dynamic viscosity, diminished infrared emissivity, retarded (T2) relaxation time in the NMR excitations, shorter spin-lattice (T1) relaxation time probed by NMR and smaller self-diffusion coefficient than bulk water (Yoo et al, 2011). All these properties imply that EZ-water is much more internally constrained than bulk water. Still, in no case does this water represent ice, it is rather a dynamically ordered entity. It is known that coherent water expels all the molecules and other particles unable to resonate with it (Preparata, 1995), so that, should we be able to keep water coherent for a time long enough, we would observe the phenomenon of solute exclusion. In normal water this phenomenon is not observed because coherent and non-coherent fractions cross over continuously one into the other producing a seemingly homogeneous time average. Close to hydrophilic surfaces, the coherent fraction of water gets somehow stabilized in time and the phenomenon of solute exclusion becomes observable. A negatively charged surface is, e.g., a polymer functionalized with sulphonic groups. Low entropy Exclusion Zone water is adjacent to the surface, and it is negatively charged with respect to bulk water. The potential difference decreases with increasing distance from the charged surface. Protons accumulate near the distant part of EZ-water; the farther the distance from the edge of EZ-water, the lower their concentration. One of the most mysterious properties of EZ-water is that of being electrically charged with respect to bulk water. EZ-water near a surface bearing a net negative charge gets negatively charged, too, and it gets positively charged near a surface with a net positive charge (Zheng et al, 2009). When negatively charged water is formed close to a negatively charged surface


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Figure 2. Schematic composition of an aqueous system near a negatively charged surface.

a zone enriched with positive charges represented by protons or hydronium ions is located beyond the EZ-water layer (Fig. 2). These protons originate from EZ-water adjacent to the negatively charged surface but they are attracted by bulk water (Chai et al, 2009). On the contrary hydroxyl ions are located close to positively charged water formed close to positively charged surfaces (Nagornyak et al, 2009). It is an intriguing feature that no opposite electric charges have been found so far between the two layers of like charges – a solid surface and an adjacent EZ-water layer. Experiments show that the potential difference between charged water and bulk water may reach more than 150 mV. Moreover, the higher the density of fixed charges on the solid surface, the deepest the thickness of charged EZ-water (Zheng et al, 2009). Notice also that though the viscosity of EZ water is higher than the viscosity of bulk water it is still a liquid. So its elements are not strongly bound to each other, and though they are likely charged they do not repel each other and do not repel the surface carrying like charges, rather they durably adhere to it. This experimental finding contradicts the electrostatic attraction/repulsion law. In the following we suggest that the “like likes like” mechanism may allow resolving this paradox.

4. What is the origin of EZ-water? Charged surfaces present usually a polymeric or crystalline backbone to which chemical residues bearing easily ionizable groups are covalently

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bound. The charge of these groups can be either positive or negative, or both. Typical examples of such surfaces are ion-exchange resins, nucleic acids where the negative charges are residues of phosphoric acid, proteins, bearing both positive and negative amino acid residues, many species of polysaccharides, and ensembles made up of different combinations of these substances. It is interesting to note that most, though not all, biopolymers carry net negative charges, sometimes with a very high negative charge density. Like charges are covalently fixed to a matrix and since each charged residue is surrounded by other charges they all vibrate. Their collective vibration tends to become coherent in order to reduce the total energy of the system. As oscillating charges become the source of an electromagnetic field, a charged surface becomes an antenna emitting oscillatory EMF with a certain degree of coherence in the surrounding space. Vibration patterns of charges depend on the intrinsic and induced vibrations of the polymeric matrix on which charged groups are fixed. This allows for a multi-mode lasing of the surface (Fig. 3). Since a charged surface is hydrophilic, when immersed into water it becomes hydrated. According to hydrogen bond models of water, in its interaction with hydrophilic surfaces the thickness of hydration water does not exceed 1-2 layers in contrast with experimental observations which show thicknesses of tens and hundreds of microns (Zheng & Pollack, 2003). Liquid water can host simultaneously several coherent oscillatory modes. These modes include electron cloud oscillation of molecules in tune with selftrapped EMF, oscillation of the electric dipoles of water molecules and oscillation of the plasmas of both negative electrical charges (quasi-free electrons) and positive electric charges (quasi-free protons). If the coherent EMF of a charged surface contiguous to water gets into a resonance with the coherent CD, the latter will be attracted by the charged surface. As an oscillating CD is approaching the oscillating charged surface, the amplitude of the oscillations of a CD is increasing since the intensity of the field increases with the square of the number of the components (Preparata, 1995). Inasmuch, as in EZ-water, there is coherence between the boundary regions of water and the wall, there will be an additional decrease of the binding energy of the pair H+-OH- in the water molecule. Consequently the gain of energy produced by the split of the water molecules into ions joining coherent ensembles becomes competitive with the binding energy of the pair H+-OH- making possible the separation of H+ and OH-. When the surface is negatively charged, and the EMF produced by it resonates with the EMF produced by oscillations of the quasi-free electrons of a CD, proton plasma cannot oscillate with the same frequency of the electron plasma because of the difference of mass between protons and electrons and therefore the positive and negative plasmas repel each other. When this


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happens, CD water converts into EZ-water. If the surface carries fixed negative charges CD water converts into negatively charged EZ-water. The picture of a negative body of water surrounded by protons then emerges (Fig. 2). The degree of coherence of oscillations of the non-compensated negative plasma of EZ-water attached to quasi-polymeric aqueous matrix is highest for the part of EZ-water closest to the oscillating charged surface. Accordingly, the negative charge density of EZ-water decreases with the increase of its distance from the charged surface. Overall charge density (electrical potential) of EZ-water and its thickness depend upon the charge density of fixed charges of the adjacent surface. This scenario explains why a negatively charged surface is coated with negatively charged water: an oscillatory EMF generated by the surface resonates with the oscillatory EMF of electron plasma CD. The resonance overcomes electrostatic repulsion of like charges since charges oscillating in unison keep together: like likes like (Fig. 3). In the case of positively charged surfaces immersed in water (for example, a polymer to which tertiary amines are covalently bound) oscillating species are protons (nuclei). The mass of a proton is about 2000-fold larger than that of an electron, so the frequency range of positive charge oscillations should lie in a range very far from the range of electron (negative charge) oscillations. This coherent EMF will attract the nearby CDs due to the resonance with quasi-free proton plasma of CDs.

Figure 3. Like charges are covalently fixed to a matrix and as each charged residue is surrounded by other charges they all vibrate. Under certain conditions (see the text) the vibrations become coherent, and the charged surface becomes the source of the coherent EMF.

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When CDs approach the surface generating the EMF due to oscillations of fixed positive charges, quasi-free electrons are ejected from coherently oscillating water (probably attached to OH- ions) and an oscillating lattice of positively charged quasi-polymeric water stays attached to the vibrating surface. In conclusion, the above discussion suggests that the presence of CDs in liquid water, predicted by QED, is the necessary and sufficient condition for the emergence of EZ-water adjacent to surfaces carrying multiple fixed charges. Such an origin of EZ-water agrees with all its experimentally determined properties. For example, the higher viscosity of EZ-water than the one of bulk water suggests that the coherent fraction in EZ-water is larger than the coherent fraction in bulk water. Indeed, coherent water is more viscous than non-coherent water since in coherent water one cannot perturb one molecule without perturbing the others. Most important, it explains why EZ-water forming near a charged surface should have the same sign of charge as the one of the surface.

5. Consequences for the living state The mere existence of EZ water is a demonstration of the realization of the principle “like likes like” based on the mechanism alternative to that suggested by Feynman implying the involvement of intermediate unlike charges (Feynman et al., 1963). This does not rule out that under certain circumstances Feynman’s mechanism advocated by N. Ise (2010) and G.H. Pollack (Nagornyak et al., 2009) may also operate. Here the interaction of different entities is fundamentally based on their ability to possess a coherent dynamics. Their “likeness” and therefore their ability to “like each other” depend here on their ability to resonate with each other. In such a case “like likes like” is equivalent to the so called “resonance attraction” (Fröhlich, 1970; Preparata, 1995). We think that the principle “like likes like” based on coherent dynamics operates in many important manifestations of biological activity. For example, it has been established that all cells of multicellular organisms have negatively charged surfaces as long as they live within their natural environments (Mehrishi & Bauer, 2002). Their negative charge is in part provided by sialic acids residues of glycoproteins covering the external surface of cell membranes of all eukariotic cells. Negative charges of cells do not interfere with their ability to attach to each other building tissues and organs. Currently this attraction of like charged cells is explained by their gluing together with special adhesive proteins, “calcium bridges”, etc. (Gumbiner, 1996). However, there is a good example of negatively charged cells sticking together where the participation of intermediate “unlikes” including molecular glue can be ruled out (Voeikov, 1998). This is the phenomenon of rouleaux formation from erythrocytes in normal blood of hu-


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mans (Fig. 4) and many animals. One can observe in taken out blood that erythrocytes which are discoids of diameter about 8 microns array themselves face to face integrating into formation looking under the microscope as stalks of coins (rouleaux). Erythrocytes are not mobile cells, so that the motion to be considered is a slow Brownian movement. Yet, Canadian hematologist S. Rowlands noticed that when two erythrocytes approach each other over a distance of about three cell radii they begin to move towards each other at a rate several times higher than in the Brownian case (Rowlands et al, 1981). This super long-range interaction between living cells is surprising from the conventional point of view on the nature of biological interactions. First, all erythrocytes are negatively charged, so they should repel rather than attract. On the other hand, if to reduce artificially charge density on their surfaces, erythrocytes do not attract and do not form rouleaux. Second, for super long-range interaction between erythrocytes certain polymeric molecules should be added to the medium: fibrinogen, polyvinylpyrrolidone, poly(ethylene oxide), dextran. These molecules are chemically very different. What unites them, so it is their high hydrophilicity, high molecular weight and fibrous structure. Rowlands states that super long-range interaction between erythrocytes cannot be explained by purely chemical forces, and suggested that human erythrocytes behaved in accordance with the major postulates of Herbert Fröhlich theory of coherent excitation in cells (Fröhlich, 1968, 1970).

Figure 4. Rouleaux formation from erythrocytes in normal human blood (courtesy from S. Rowlands).

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He proposed that “perhaps the property of macromolecule which mediates the transmission of the Frohlich interaction is the molecule’s ability to polarize the surrounding water into oriented, ordered multilayers, along which Frohlich polar waves could be transmitted.” (Rowlands, 1988). Significance of purely chemical forces after erythrocytes had associated into rouleaux is undermined also by the observation that cells do not stick to each other chemically – the closest distance between neighboring cells is of the order of 1 micron, – several orders of magnitude more than any chemical bond may provide. Chemical models cannot explain fractional separation of erythrocytes of different species in their mixed suspensions in polyvinylpyrrolidone 360 kDa solutions. For example, in the mixture of human, cat, dog, mice, rat, rabbit, and guinea pig erythrocytes they tended to aggregate into rouleaux significantly enriched with cells of one particular species. Only rat and mice erythrocytes did not distinguish each other probably because these two species are close relatives (Forsdyke D.R., Ford P.M., 1983) (Sewchand L., Canham P.B., 1976.). On the other hand, long-distance interactions based on vibrations of interacting entities should be highly specific, as they should depend on frequencies or phases of the vibrations. Such discrimination is a good illustration of the principle “like likes like” following from the resonance attraction of emitters of EMF with similar patterns. Specific interaction of erythrocytes may be one of many examples of specific interactions of like charged cells. Rowlands suggests that very fast interaction of platelets in the case of their stimulation in vitro may also be explained by Frohlich interaction (Rowlands, 1988). We could consider this red cells attraction as a manifestation of the general principle of Resonance Attraction, like likes like. The coupling of coherent systems, such as living organisms, with electromagnetic potentials allows long-range correlations among coherent systems very far away from each other. This provides for the realization of continuous and practically immediate interactions both within individual living systems and between them.

Conclusion In this chapter we have presented the coherent dynamical mechanisms implied by QED in water and shown how they open new horizons in biology (and not only in biology). The problem of the interconnectedness of the parts of an organism, the synchronization of their workings, the self-governance of biochemical processes, whose explanation is lacking in the biochemistry frame based solely on the random kinetics of molecular reactions, finds a natural understanding in our approach. The successful estimate of thermodynamic variables (specific heats, latent heats, entropy) of liquid water, as


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shown in (Arani et al. 1995), opens a promising pathway for the future. A special role appears to be played by the interaction of different entities based on their ability to possess coherent dynamics allowing them to resonate with each other. Then their “likeness” turns out in their ability to “like each other”. This is similar (although different in several dynamical aspects and details) to the so called “resonance attraction” (Fröhlich, 1970; Preparata, 1995). The actual description of biological dynamics in quantitative terms appears to us the next task for our research. Much work needs still to be done in such a direction. However, the present stage of our analysis already leads us to the conclusion that quantum physics, as formally expressed in rigorous terms by QED, plays a crucial role in living matter, so that we can only agree with Schrödinger warning that the ''regularities only in the average'' (Schrödinger, 1944, p.78) emerging from the ''statistical mechanisms'' are not enough to explain the ''enigmatic biological stability'' (ibidem p.47). Pretending to explain the biological functional stability in terms of the regularities of statistical origin would be the ''classical physicist's expectation'' that ''far from being trivial, is wrong'' (ibidem p.19).

References Aharonov, Y. & Bohm, D. 1959. Significance of electromagnetic potentials in quantum theory. Phys. Rev. 115: 485–491. Aharonov, Y., & Bohm, D. 1961. Further considerations on electromagnetic potentials in the quantum theory. Phys. Rev. 123: 1511–1524. Arani, R., Bono, I., Del Giudice, E. & Preparata, G. 1995. QED coherence and the thermodynamics of water. Int. J. Mod. Phys. B. 9: 1813–1841. Askaryan, G. 1962. Effects of the gradient of strong electromagnetic beam on electrons and atoms. Soviet Physics JETP-USSR 15: 1088–1090. Barbieri, M. (2002) The organic codes: an introduction to semantic biology. Cambridge University Press. Beige, A., Knight, P.L. & Vitiello, G. 2005. Cooling many particles at once. New J. Phys. 7: 96. Blasone, M., Jizba, M. & Vitiello, G. (2011) Quantum Field Theory and its Macroscopic Manifestations. Imperial College Press. Bono, I, Del Giudice, E, Gamberale, L & Henry, M. Emergence of the coherent structure of liquid water. 2012. Water. 4: 510–532; doi:10.3390/w4030510. Celaschi, S. & Mascarenhas, S. 1977. Thermal-stimulated pressure and current studies of bound water in lysozyme. Biophys. J. 20: 273–277. Chai, B., Yoo, H. & Pollack, G.H. 2009. Effect of radiant energy on near-surface water. J Phys Chem B. 113: 13953–13958. Del Giudice, E. &Vitiello, G. 2006. Role of the electromagnetic field in the formation of domains in the process of symmetry-breaking phase transitions. Phys. Rev. A. 74: 022105.

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Del Giudice, E., & Preparata, G. 1998. Electrodynamical like charge attractions in metastable colloidal crystallites. Modern Physics Letters B. 12: 881–885. Del Giudice, E., Preparata, G. & Fleischmann, M. 2000. QED coherence and electrolyte solutions. J. of Electroanalytical Chem. 482: 110–116. Del Giudice, E., Preparata, G. & Vitiello, G. 1988. Water as a free electric dipole laser. Phys. Rev. Lett. 29: 1085–1088 Del Giudice, E., Doglia, S., Milani, M. & Vitiello, G. 1985. A quantum field theoretical approach to the collective behaviour of biological systems. Nucl. Phys. B 251 (FS 13): 375–400 Del Giudice, E., Doglia, S., Milani, M. & Vitiello, G. 1986. Electromagnetic field and spontaneous symmetry breaking in biological matter. Nucl. Phys. B 275 (FS 17): 185– 199. Edelman G. 1984. Cell-adhesion molecules: a molecular basis for animal form. Scientific American, 250: 118–129. Feynman, R. P., Leighton, M. & Sands, M. (1963) The Feynman Lecture on Physics. Addison-Wesley, Reading, MA. pp. 2–3, ch. 2. Forsdyke D.R. & Ford P.M, 1983. Segregation into separate rouleaux of erythrocytes from different species. Evidence against the agglomerin hypothesis of rouleaux formation. Biochem J. 214: 257–260. Franks, F. (1972-1982). Water, a Comprehensive Treatise. (7 volumes) Plenum: New York, NY, USA,(7 volumes). Fröhlich, H. 1968. Long range coherence and energy storage in biological systems. Int. J. Quantum Chem. 2: 641–649. Fröhlich, H. 1970. Long Range Coherence and the action of enzymes. Nature. 228: 1093. Gumbiner, B.M. 1996. Cell adhesion: The molecular basis of tissue architecture and morphogenesis. Cell. 84: 345–357. Gurwitsch, A.G. & Gurwitsch, L.D. 1943. Twenty years of mitogenetic radiation. Uspechi Biol. Nauk. 16: 305-334. (English translation: Gurwitsch, A.G. & Gurwitsch, L.D. 1999. 21st Century Science & Technology. 12: No 3, 41–53.) Ise, N. 2010. Like likes like: counterion-mediated attraction in macroionic and colloidal interaction. Phys. Chem. Chem. Phys. 12: 10279–10287. Larsen, A. E., & Grier, D. G. 1997. Like charge attractions in metastable colloidal crystallites, Nature. 385: 230–235. Mehrishi, J.N. & Bauer, J. 2002. Electrophoresis of cells and the biological relevance of surface charge. Electrophoresis. 13: 1984–1994. Nagornyak, E., Yoo, H., & Pollack, G.H. 2009. Mechanism of attraction between likecharged particles in aqueous solution. Soft Matter. 5: 3850–3857. Nernst, W. 1916. Über einen Versuch, von quantentheoretischen Betrachtungen zur Annahme stetiger Energieänderungen zurückzukehren. Verh. Deutsche Physikalische Gesellschaft. 18: 83–116. Popp, F.-A. (1979) Photon Storage in Biological Systems. In: Electromagnetic Bioinformation. (Popp, F.A., Becker, G., König, H.L. & Peschka, W. eds.). pp. 123–149. Urban & Schwarzenberg Munchen-Wien-Baltimore. Preparata, G. (1995) QED, Coherence in Matter. World Scientific, Singapore-London-New Jersey.


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Putterman, S. & Weniger, K. 2000. Sonoluminescence: how bubbles turn sound into light. Ann. Rev. Fluid Mechanics. 32: 445–476. Rowlands, S., Sewchand, L.S., Lovlin, R.E., Beck, J.S. & Enns, E. G. 1981. A Fröhlich interaction of human erythrocytes. Phys. Lett. A. 82: 436–438. Rowlands S. (1988) The interaction of living red blood cells. In: Biological Coherence and Response to External Stimuli. (Herbert Frohlich, ed. Springer Verlag, Berlin, Heidelberg, pp. 171–191 Schrödinger, E. (1944). What is life? (1967 reprint) Cambridge University Press: Cambridge. Sewchand L. & Canham P.B., 1976. Induced rouleaux formation in interspecies populations of red cells. Can. J. Physiol. Pharmacol. 54: 437–442. Szent-Gyorgyi, A. (1960) Introduction to a Supramolecular Biology; Academic Press: New York, NY, USA. Voeikov, V.L. 1998. Physical-chemical and physiological aspects of erythrocyte sedimentation reaction. Usp. Physiol. Sci. (Progress in Physiology, Moscow). 29: 55–73. Voeikov, V.L. & Del Giudice, E. 2009 Water Respiration – The Basis of the Living State. WATER; A Multidisciplinary Research Journal. 1: 52–75. Yoo, H., Paranji, R., & Pollack, G.H. 2011. Impact of hydrophilic surfaces on interfacial water dynamics probed with NMR spectroscopy. J Phys Chem Lett. 2: 532–536. Zheng, J.M., & Pollack, G.H. 2003. Long range forces extending from polymer surfaces. Phys Rev E. 68:031408.10.1103/PhysRevE.68.031408 Zheng, J.M., Chin, W.C., Khijniak, E., Khijniak, E. Jr. & Pollack, G.H. 2006. Surfaces and interfacial water: evidence that hydrophilic surfaces have long-range impact. Adv. Colloid Interface Sci. 23: 19–27. Zheng, J.M., Wexler, A., & Pollack, G.H. 2009. Effect of buffers on aqueous solute-exclusion zones around ion-exchange resins. J Colloid Interface Sci. 332: 511–514.

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Chapter 6

The photon source within the cell Ankush Prasad and Pavel Pospíšil Department of Biophysics, Centre of the Region Haná for Biotechnological and Agricultural Research Faculty of Science, Palacký University, Šlechtitelů, 783 71 Olomouc, Czech Republic Abstract: Reactive oxygen species (ROS) are strong oxidants known to oxidize electron-rich organic molecules including lipids, proteins and nucleic acids. The oxidation of lipids initiated by radical ROS (superoxide anion, perhydroxyl and hydroxyl radicals), non-radical ROS (singlet oxygen, hydrogen peroxide) or by enzymatic reaction pathway (lipoxygenase) results in the formation of peroxyl radicals. The re-combination of two peroxyl radicals via Russell mechanism and cyclization of peroxyl radicals form linear tetroxide and cyclic dioxetane intermediates that decompose to the triplet excited carbonyls [3(R=O)*] and singlet oxygen (1O2). The experimental evidence indicates that these electronically excited species are the main emitters responsible for ultra-weak photon emission from biological systems. In addition to lipid peroxyl radicals, other biological peroxyl radicals including peroxyl radicals in protein and nucleic acids may participate in the ultraweak photon emission from biological systems. The review attempts to focus on the current knowledge on the involvement of ROS-induced oxidation of biomolecules in the biological systems. Correspondence/Reprint request: Dr. Pavel Pospíšil, Department of Biophysics, Centre of the Region Haná for Biotechnological and Agricultural Research, Faculty of Science, Palacký University, Šlechtitelů, 783 71 Olomouc, Czech Republic. E-mail: [email protected]

1. Introduction 1.1. Definition of ultra-weak photon emission All living organisms emit spontaneous ultra-weak photon emission as a result of oxidative cellular metabolic processes. It is differentiated from the phenomenon of delayed luminescence as it is spontaneously emitted by living organism without any photoexcitation (Slavinski et al., 1992, Kim, 2005). The intensity of light emitted is 1000 times lower than the sensitivity

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of the human eye (Sauermann et al., 1999; Kobayashi, 2003). The ultraweak photon emission is dependent on the elementary biochemical reactions in the living system and is considered as a by-product of metabolism (Popp et al., 1988; Slavinski et al., 1992, Kobayashi et al., 1999). Besides the biochemical theory of ultra-weak photon emission, Fritz-Albert Popp proposed the origin of ultra-weak photon emission from DNA and later its coherence properties (Popp et al., 1988). The ultra-weak photon emission is also referred to as biophoton emission, biological chemiluminescence, low-level chemiluminescence or autoluminescence (Lavorel, 1980; Wijk et al., 1988; Hideg et al., 1990; Kobayashi et al., 1997; Yan et al., 2003; Havaux et al., 2006; Rastogi and Pospíšil, 2010; Wijk et al., 2010; Cifra et al., 2010).

1.2. Historical aspects of ultra-weak photon emission Photosynthetic organisms such as Chlorella, Stichococcus and Scenedesmus have been demonstrated to emit photons in the visible region of the spectrum spanning from 400–700 nm (Strehler and Arnold, 1951). The research on photon emission from microorganisms was also done by Konev et al., (1966) from synchronized cells of Candida utilis using UV-sensitive PMT. The photon emission observed was in the spectral range of 250–380 nm and was claimed to be originated from cell division. Quickenden et al., (1985, 1991) have shown photon emission from yeast cells, Saccharomyces sp. under normal growth conditions. However, under anaerobic condition, no photon emission was observed from different species of yeast cells. Thus, the ultra-weak photon emission from living organism is described in two different regions of the spectrum comprising of the ultra-violet region and the visible region of the spectrum (Konev et al., 1966; Popp et al., 1992; Shen et al., 2005; Beloussov et al., 2007; Cifra et al., 2010, Prasad and Pospíšil, 2013). However, the ultra-weak photon emission in the ultra-violet region is not completely understood. Currently, several research groups around the globe are working on the topic to better understand the origin and mechanism together with spectral and intensity properties of photon emission and physiological significance which includes the phenomenon of optical cell communication via ultra-weak photon emission.

1.3. Involvement of ROS in ultra-weak photon emission The ultra-weak photon emission in the visible region of spectrum is well described and claimed to be originated via processes such as lipid peroxidation, protein and nucleic acid oxidation while the photon emission in the UV region remained unexplored (Allen et al., 1972; Kobayashi et al., 1999; Prasad and Pospíšil, 2011; Boveris et al., 1976, Wijk et al., 2010, Kobayashi et al.,1999). Boveris et al. (1980) demonstrated the dependence of chemiluminescence on oxygen concentration. It was observed that the addition of exoge-

Photon source within the cell

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nous hydrogen peroxide (H2O2) (abbreviations of reactive oxygen species are listed in Table 2) to the perfused rat liver enhanced the chemiluminescence. It was claimed that oxidative radical reaction leads to the generation of chemiluminescence. Later during the 90’s, Slavinski and co-workers (1992) studied the correlation between oxidative stress and ultra-weak photon emission. It was demonstrated that stress factors and pathological states affect characteristics of photon emission such as spectra and intensity. Recently, it has been shown that 1) the addition of exogenous ROS to living cells or enhancement of endogenous ROS formation as monitored by spintrapping electron paramagnetic resonance (EPR) spectroscopy induces a high degree of oxidative stress leading to enhanced ultra-weak photon emission (Khabiri et al., 2008; Rastogi and Pospíšil, 2010; Prasad and Pospíšil, 2011, Rastogi and Pospíšil, 2011; Rastogi and Pospíšil, 2012), 2) the addition of ROS scavengers known to eliminate oxidative stress caused decrease in ultra-weak photon emission (Rastogi and Pospíšil, 2011; Prasad and Pospíšil, 2011), and 3) aerobic, hyperaerobic and anaerobic conditions modulate ultra-weak photon emission (Nakamura et al., 2005; Rastogi and Pospíšil, 2011) (Table 1).

2. Experimental evidence on the involvement of ROS in ultra-weak photon emission It is well known that ultra-weak photon emission is an intrinsic property of all biological systems involving animals, plants and microorganisms. Figures 1-3 show two-dimensional imaging of spontaneous ultra-weak photon emission from human hand, higher plant Arabidopsis thaliana, and yeast cells, Saccharomyces cerevisiae. In addition, there is strong evidence about the involvement of ROS in ultra-weak photon emission in animals, plants and microorganisms as discussed in next sub-sections and Table 1.

2.1. Ultra-weak photon emission in animals Using a sensitive photomultiplier tube (PMT), enhancement in onedimensional ultra-weak photon emission was shown from the human hand under the influence of temperature and oxygen concentration (Nakamura and Hiramatsu, 2005). It was proposed that an increase in temperature enhances spontaneous ultra-weak photon emission. Hagens et al., (2008) observed that there is a decrease in ultra-weak photon emission with increasing humidity, oxygen concentration and pH.

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Experiment

Results

Reactive oxygen species (ROS) as enhancers of ultra-weak photon emission

Enhancement in ultra-weak photon emission was observed upon addition of hydrogen peroxide (H2O2) to rat perfused liver (Boveris et al. 1976). Khabiri et al. (2008) correlated H2O2 and ultra-weak photon emission in bovine serum albumin. Rastogi and Pospíšil P (2010) and Prasad and Pospíšil (2011) have shown enhancement in ultra-weak photon emission upon topical application of different ROS on the human hand.

Scavenger of ROS as suppressors of ultra-weak photon emission

The study of Rastogi and Pospíšil (2011) demonstrated that the topical application of different scavengers such as alpha-tocopherol, glutathione and ascorbate on the human hand suppress ultra-weak photon emission.

Influence of gases in the environment

Boveris et al., (1976) demonstrated dependence of ultra-weak photon emission on the oxygen concentration of the system in organelle of the rat. Nakamura et al., (2005) demonstrated influence of temperature and oxygen concentration on ultra-weak photon emission in human hand. Rastogi and Pospíšil (2011) have shown the dependence of aerobic and anaerobic condition on ultra-weak photon emission from the human hand.

Table 1. Experimental evidence for the involvement of reactive oxygen species (ROS) in ultra-weak photon emission.

It has also been observed that UVA radiation induces ultra-weak photon emission especially in the deeper skin layers. Khabiri et al., (2008) demonstrated a correlation between concentration of H2O2 in aqueous bovine serum albumin and ultra-weak photon emission. It was observed that in the presence of H2O2, the oxidation of albumin leads to the formation of protein carbonyls compounds. Also, it was demonstrated that ultra-weak photon emission is generated from oxidation of amino acids such as Phe, Trp, His and Cys. Using highly sensitive charge coupled device (CCD) imaging system, Kobayashi et al., (1999) demonstrated that the intensity of ultra-weak photon emission in rat’s brain is associated with the cerebral blood flow and the hyperoxia measured using electroencephalographic activity. It was further demonstrated that the removal of glucose suppressed the photon emission while the addition of potassium ions enhanced the ultra-weak photon emission. This led to the conclusion that ROS formed in the inner mitochondrial membrane are involved in the photon emission. It has been recently demon-


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strated that the topical application of ROS on the dorsal surface of the hand resulted in an enhancement in ultra-weak photon emission (Prasad and Pospíšil, 2011). The authors claimed that processes such as lipid peroxidation and protein oxidation were responsible for enhancement in ultra-weak photon emission as monitored by highly sensitive CCD camera and low-noise PMT. The topical application of different scavengers such as α-tocopherol, coenzyme Q10, ascorbate and glutathione which are known eliminate ROS was studied on human skin (Rastogi and Pospíšil, 2011). It was observed that the photon emission from hand was considerably suppressed by the topical application of scavengers thereby demonstrating the role of ROS in ultra-weak photon emission. Furthermore, the author demonstrated that the ultra-weak photon emission from the hand was greatly enhanced and suppressed under hyperaerobic and hypoaerobic environment, respectively. The involvement of skin chromphores in ROS-mediated ultra-weak photon emission was studied upon visible and UVA radiation (Prasad and Pospíšil, 2012). A comparative study of the exposure of dorsal and palmar side of the human hand revealed that UVA radiation generates higher oxidative stress compared to visible light. Based on these data, the authors proposed that the ultra-weak photon emission is linked to the oxidation of lipids which proceeds via type I and type II photosensitization mechanisms. It has been previously demonstrated, employing immunoblotting techniques that exposure of the human skin to UV radiation results in the formation of 3(R=O) in the human stratum corneum (Thiele et al., 1998; Sander et al., 2001). It is well established that 3(R=O)* emits photons in the blue-green region of the spectrum ranging from 400–550 nm, whereas the dimol emission from 1O2 has been demonstrated in the red region of the spectrum at 634 nm and 703 nm. It has been observed that UV radiation-induced ultra-weak photon emission in the human skin bears photon emission maxima in the spectral range of 400–580 nm supporting the assumption that 3(R=O)* is a main source of ultra-weak photon emission after the exposure of the human skin to UV radiation (Thiele et al., 1998; Khabiri et al., 2008). The spontaneous ultra-weak photon emission from the human hand has also been shown to be maximal at 500 nm (van Wijk and van Wijk, 2005). However, the spectral analysis of spontaneous ultra-weak photon emission from the human skin indicated that photons are spontaneously emitted mainly at the red region of the spectrum revealing that 1O2 predominantly contributes to the photon emission (Sander et al., 2001; Federova et al., 2007; Harada et al., 2009).

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O2•-

superoxide anion racial

ROOR

dioxetane

H2O2

hydrogen peroxide

ROOOOR

tetroxide

1O 2

singlet oxygen

ROOH

hydroperoxide

HO•

hydroxyl radical

ROH

organic hydroxide

R•

alkyl radical

R=O

ground state carbonyl

ROO•

peroxyl radical

3(R=O)*

Triplet excited carbonyl

RO•

alkoxyl radical

1Chr*

Singlet excited chromophore

3Chr*

Triplet excited chromophore

1Chl*

Singlet excited chlorophyll

3Chl*

Triplet excited chlorophyll

Table 2. The table displays abbreviations of reactive oxygen species (ROS), lipid and protein radicals, reactive intermediates and electronically excited species involved in ultra-weak photon emission.

A

B

Figure 1. Two-dimensional imaging of the spontaneous ultra-weak photon emission from the hand. The photograph (A) shows the dorsal side of a hand and (B) its corresponding image of ultra-weak photon emission. Ultra-weak photon emission imaging was measured with an integration time of 30 min and the hand was distanced at 37 cm from CCD camera window.

2.2. Ultra-weak photon emission in plants Using a highly sensitive CCD camera, enhancement in two-dimensional ultra-weak photon emission was shown after the mechanical wounding of


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Arabidopsis thaliana leaves (Henry et al., 2004). The authors proposed that ROS-related lipid peroxidation might be responsible for photon emission. Contrarily, ultra-weak photon emission observed in Arabidopsis thaliana leaves infected by fungi Pseudomonas syringea was related to the reactive nitrogen species (RNS)-related gene-for-gene mediated hypersensitive cell death; however, the participation of ROS-induced lipid peroxidation was not completely ruled out (Bennett et al., 2005). Mansfield (2005) demonstrated a correlation between the hypersensitive reaction leading to the generation of RNS and the lipid peroxidation leading to the ultra-weak photon emission. The correlation between the ultra-weak photon emission and the formation of secondary end product of lipid peroxidation malondialdehyde (MDA) measured in the photoresistant wild type and double mutant Arabidopsis thaliana lacking both ascorbate and zeaxanthin confirmed the involvement of lipid peroxidation in ultra-weak photon emission (Havaux et al., 2006). Kobayashi et al., (2007) have demonstrated that the resistance of plants to pathogen infection induces a high level of ultra-weak photon emission via the generation of ROS. The ultra-weak photon emission was regarded to result from the oxidation of lipids, proteins and nucleic acids. Using ROS scavengers, it was demonstrated that ROS generated during the oxidative burst of hypersensitive reaction are involved in the ultra-weak photon emission. The addition of H2O2 to the isolated radish root cells caused a significant enhancement in ultra-weak photon emission, whereas the removal of molecular oxygen using glucose/glucose oxidase system and free oxygen radical using sodium ascorbate and cysteine completely suppressed the ultraweak photon emission. The comparison of ultra-weak photon emission and electron paramagnetic resonance spin-trapping data showed that the photon emission observed after the addition of H2O2 correlates with the formation of 1O2 (Rastogi and Pospíšil, 2010). It has been recently demonstrated that the lipid peroxidation initiated by the addition of exogenous linoleic acid to the unicellular green alga Chlamydomonas reinhardtii cells was accompanied by an enhancement in ultra-weak photon emission as monitored by highly sensitive CCD camera and low-noise PMT (Prasad and Pospíšil, 2011). The enhancement in ultraweak photon emission observed after the addition of linoleic acid to the Clamydomonas cells was shown to correlate with the accumulation of lipid peroxidation product as measured using thiobarbituric acid assay. The observation that the addition of mannitol which acts as HO• scavenger, caused a pronounced suppression in ultra-weak photon emission reveals that HO• is involved in ultra-weak photon emission. The authors proposed that HO• formed by the reduction of H2O2 by free metals through Fenton-type chemistry initiates lipid peroxidation leading to the formation of electronically excited species such as 3(R=O)*, 1O2 and 1Chl*.

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The observation that the removal of molecular oxygen using enzyme system glucose/glucose oxidase results in a significant suppression in ultra-weak photon emission indicates that molecular oxygen is involved in ultra-weak photon emission (Prasad and Pospíšil, 2011). The involvement of intrinsic enzyme lipoxygenase in the process of lipid peroxidation was shown by monitoring the effect of catechol, an inhibitor of lipoxygenase. The observation that the addition of catechol to Clamydomonas cells results in the suppression of ultra-weak photon emission indicates the role of lipid peroxidation in ultraweak photon emission. This proposal was confirmed by the finding that no photon emission was observed in lipoxygenase-deficient Arabidopsis mutant (LOX2i-2 and LOX2i-9) exposed to the mechanical injury and high light stress (Birtic et al., 2011). Based on the experimental evidence discussed, several authors proposed that the ultra-weak photon emission is linked to the lipid peroxidation, which proceeds via the non-enzymatic reaction pathway mediated by free oxygen radicals or via the enzymatic reaction pathway mediated by lipoxygenase. Evidence has been provided that the final emitters are 3(R=O)* and 1O2 in non-chlorophyll-containing part of plant, whereas in the chlorophyllcontaining part of plant, the photons are emitted mainly by chlorophyll and partly contributed by 3(R=O)* and 1O2. Spectral properties of the H2O2induced ultra-weak photon emission from isolated radish root cells show that the photons are emitted in the spectral range from 450 nm to 800 nm (Rastogi and Pospíšil, 2010). These observations indicate that the photon emission in the blue-green region (400-550 nm) originates from 3(R=O)*. The comparison of spectra of ultra-weak photon emission from isolated spinach mitochondria and linoleic acid/lipoxygenase reveals that 1O2 formed during the lipid peroxidation mainly contributes to photon emission (Hideg et al., 1991). In chlorophyll-containing samples, chlorophylls are regarded as the main emitters. The finding that the photon emission induced by the addition of exogenous linoleic acid to the unicellular green alga Chlamydomonas cells is negligible in the blue-green region of the spectrum shows that 3(R=O)* does not contribute significantly to the ultra-weak photon emission from unicellular green alga Chlamydomonas cells (Prasad and Pospíšil, 2011). The observation that the photon emission dominated at the red region of the spectrum reveals the involvement of chlorophylls and 1O2 in ultra-weak photon emission. The detailed exploration of spectral properties in the red region of the spectrum showed that the maximum photon emission is at 680 nm which corresponds to the photon emission of chlorophylls (Prasad and Pospíšil, 2011). Similarly, the spectral properties of ultra-weak photon emission from cowpea infected with mosaic virus shows that the photons are emitted predominantly in the red region of the spectrum indicating that chlorophylls serves as the final emitter of photons during hypersensitive response (Kobayashi et al., 2007). These considerations reveal that in the


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chlorophyll-containing sample, the excitation energy from 3(R=O)* is transferred to chlorophylls. In the triplet-singlet energy transfer mechanism, the triplet excitation energy from 3C=O* is transferred to chlorophyll forming singlet excited chlorophyll (1Chl*). In the triplet-triplet energy transfer mechanism, the triplet excitation energy from 3C=O* is transferred to chlorophyll forming the triplet excited chlorophyll (3Chl*) which is converted to 1Chl* by reverse intersystem crossing.

A

B

Figure 2. Two-dimensional imaging of the spontaneous ultra-weak photon emission from Arabidopsis thaliana. The photograph of Arabidopsis plant (A) and, its corresponding image of ultra-weak photon emission (B). Ultra-weak photon emission imaging was measured with an integration time of 30 min with the Arabidopsis plant distanced at 25 cm from CCD camera window.

2.3. Ultra-weak photon emission in microorganisms The detection of ultra-weak photon emission from microorganism was first detected by Konev et al., (1966) from synchronized cells of Candida utilis using UV-sensitive PMT. The photon emission observed in the spectral range of 250–380 nm was claimed to originate from cell division. The authors also studied and performed pioneering work with ultra-weak photon emission from microorganisms including yeast cells and bacteria. On contrary, Stauff and co-workers (1964) with their experiments on yeast cells, Saccharomyces cereviseae observed the photon emission dependence only on the availability of molecular oxygen irrespective of stage and viability of the cells. The ultra-weak photon emission research from microorganisms was

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taken further by Quikendem et al., (1985), which reported that, the photon emission from Saccharomyces cells comprises visible and UV component.

A

B

Figure 3. Two-dimensional imaging of the spontaneous ultra-weak photon emission from Saccharomyces cereviseae. The photograph (A) depicts the culture of S. cereviseae and, while (B) shows its corresponding images of ultra-weak photon emission. Ultra-weak photon emission imaging was measured with an integration time of 30 min with the Saccharomyces cells distanced at 25 cm from CCD camera window.

Roth and Kaeberle (1980) measured bacteria such as Escherichia coli and Klabsiella pseumoniae but no photon emission was observed. However, they observed one major peak during the exponential growth phase of Listeria monocytogenes and found that superoxide dismutase and catalase inhibited the photon emission. On the other hand, the application of HO• brings about no change in photon emission. Thus, O2•- and H2O2 were claimed to be involved in photon emission.

3. Mechanism of ultra-weak photon emission 3.1. Oxidation of biomolecules 3.1.1. Cycloaddition of singlet oxygen The reaction of 1O2 with biomolecules such as lipids and proteins leads to the formation of dioxetane (ROOR) intermediate (Di Mascio et al., 1992). The ROOR decomposes to 3(R=O)* with the release of secondary by-products such as ground carbonyl R=O and organic hydroxide (ROH). Triplet excited carbonyls are known to either emit photons or further transfer the excita-


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tion energy to molecular oxygen forming 1O2 (Di Mascio et al., 1992; Timmins et al., 1997) (scheme 1).

3.1.2. Hydrogen abstraction In the non-enzymatic reaction pathway, the free oxygen radical readily abstracts a hydrogen atom from lipids and proteins, leading to the formation of alkyl radical (R•). Alkyl radical reacts with molecular oxygen at the diffusionlimited rate to form peroxyl radical (ROO•). In the enzymatic reaction pathway, the oxidation of lipids is catalyzed by lipoxygenase (Brash et al., 1999; Maccarrone et al., 2001; Halliwell and Gutteridge, 2007). The first step in the enzymatic reaction is the abstraction of the hydrogen atom from carbon by the ferric non-heme iron of the enzyme (Fe3+–OH) to generate R•, whereas the active site of the enzyme is reduced to the ferrous non-heme iron (Fe2+–OH2). The interaction of R• with molecular oxygen results in the formation of ROO• and the re-oxidation of ferrous non-heme iron (Fe2+–OH2) to ferric non-heme iron (Fe3+–OH) (Nelson, 1988; McGinley and Donk, 2003).

3.2. Self-recombination of organic radicals 3.2.1. Cyclization and self-recombination of peroxyl radicals The cyclization and self-recombination of ROO• leads to the formation of a reactive intermediate such as ROOR or tetroxide (ROOOOR), respectively (scheme 1). The ROO•, besides its recombination reaction, can also react with other biomolecule forming hydroperoxide (ROOH) which subsequently, in the presence of metal ions such as Fe2+, can form alkoxyl radical (RO•). The ROOOOR formed upon ROO• recombination can either form 3(R=O)* with the release of secondary by-product ROH and molecular oxygen or can form 1O2 and R=O via Russell mechanism. The ROOR decomposes to 3(R=O)* with the release of secondary by-products ROH (Dean et al., 1997; Federova et al., 2007).

3.2.2. Self-recombination of alkoxyl radical The self-reaction of RO• in the presence of molecular oxygen might theoretically leads to the formation of ROOOOR which can either decompose to form 3(R=O)* with the release of secondary by-product ROH and molecular oxygen or can form 1O2 and R=O.

3.3. Excitation energy transfer to chromophores Triplet excited carbonyl can directly emit photons in the wavelength range of 400–550 nm with the formation of R=O, or can transfer excitation energy either to molecular oxygen or to chromophores (scheme 1) (Bohne et al., 1986; Wondrak et al., 2006).

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Scheme 1. Mechanism of generation of electronically excited species by oxidation of biomolecules. Abstraction of hydrogen atom from biomolecules (lipids, proteins and nucleic acid) (RH) results in the formation of alkyl radical (R•). Alkyl radical reacts with molecular oxygen to generate a peroxyl radical (ROO•). Peroxyl radical reacts with the adjacent ROO• by the Russell-type mechanism forming reactive intermediate tetroxide (ROOOOR) or reacts with RH forming hydroperoxide (ROOH) which further in the presence of metal ions (Fe2+) forms alkoxyl radical (RO•) which thereby recombine together forming ROOOOR. The cycloaddition can either lead to the formation of hydroperoxide (ROOH) or dioxetanes (ROOR). The ROOH formed can either form ROOOOR as described above or can form ROOR via RO• formation. Tetroxide further decomposes to carbonyl, molecular oxygen and hydroxides (ROH). The carbonyls are formed either in the triplet excited 3(R=O)* or the ground (R=O) state, whereas molecular oxygen is correspondingly in the triplet ground state (3O2) or the singlet excited state (1O2). ROOR decomposes forming 3(R=O)*, ROH and molecular oxygen. The excitation energy transfer from 3(R=O)*to chromophores molecules results in the formation of excited state of chromophore (Chr*). An electronic transitions from the triplet excited state to the ground state of carbonyls is accompanied by the emission of photons in the blue-green region spanning from 400–550 nm of the spectrum while 1O2 dimol emission is accompanied by photon emission in the red region of the spectrum at 634 and 703 nm.


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The excitation energy transfer to molecular oxygen leads to the formation of 1O2 known to provide dimol emission at the wavelength of 634 nm and 703 nm. Besides this, the excitation energy can be transferred to chromophores such as chlorophyll, melanin, urocanic acid, porphyrin, bilurubin, flavins and pterins.

4. Application of ultra-weak photon emission for the study of oxidative processes The ultra-weak photon emission can be a useful non-destructive tool to follow the extent of lipid peroxidation and protein oxidation in animals, plants and microorganisms in-vivo. Detection of ultra-weak photon emission, which provides both the spatial and temporal information opens new possibilities to better characterize the response of animals, plants and microorganisms to various stresses. The direct detection of ultra-weak photon emission using highly sensitive CCD camera, which provides information on the twodimensional distribution of the photon emission in the sample, characterizes the different responses of animals, plants and microorganisms to the stress factors in the different parts of the body. Low-noise PMT provides information on the kinetics of ultra-weak photon emission, enables to follow the temporal characteristics of the response to the environmental stress factors in all kind of living systems. The use of ultra-weak photon emission as a non-invasive diagnostic tool for monitoring of biomolecules oxidation helps to better understand the mechanistic insights into the response of organism to the numerous abiotic and biotic stresses.

Summary Ultra-weak photon emission is a result of oxidative radical reactions via the oxidation of lipids, proteins and nucleic acids in the cellular system. The oxidative radical reactions are prone to fluctuations in physical and chemical factors and thus ROS-mediated ultra-weak photon emission is influenced by an alteration of such factors. The mechanism of ultra-weak photon emission has been described in the scheme 1, where electronically excited species such as 3(R=O)*, chromophores and 1O2 are held responsible for the ultraweak photon emission. The ultra-weak photon emission has been shown to be variable based on the composition of chromophores present in the system (Prasad and Pospíšil, 2011; Prasad and Pospíšil, 2012). Due to the ultraweak nature of photon emission which is known to be in the order or 105– 106 photons/cm2 s-1, low-noise PMT and highly sensitive CCD camera are being utilized for the precise detection of the ultra-weak photon emission. In the past, different techniques such as spin-trapping EPR spectroscopy, im-

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munoflorescence spectroscopy and calorimetry have been used for deciphering the role of ROS in ultra-weak photon emission. The ultra-weak photon emission is thus helpful in monitoring the physiological status of an organism and quality control in food industry.

Acknowledgement The work was supported by Ministry of Education, Youth and Sports of the Czech Republic Grants No.LO1204 (National Program for Sustainability I), No. CZ.1.07/2.3.00/20.0057 (Progress and Internationalization of Biophysical Research at the Faculty of Science, Palacký University), No. CZ.1.07/2.3.00/ 30.0041 (Support for Building Excellent Research Teams, Intersectoral Mobility at Palacký University) and Grant Agency of the Czech Republic Grant No. GP13-29294S. We thank Prof. Jiří Hašek (Institute of Microbiology, Academy of Sciences, Czech Republic) and Dr. Michal Cifra (Institute of Photonics and Electronics, Academy of Sciences, Czech Republic) for providing yeast cell culture.

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Chapter 7

Photon emission in multicellular organisms 1,2

2

Eduard Van Wijk , Yu Yan and Roeland Van Wijk

1,2

1

Sino-Dutch Centre for Preventive and Personalized Medicine, Leiden University, The Netherlands, Meluna Research, The Netherlands

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Abstract: Ultra-weak photon emission (UPE) from biological systems was first demonstrated, in the early 1960’s, by Russian researchers utilizing sensitive photomultiplier equipment. However, already in 1912 a proposal for a morphogenetic radiation field, responsible for regulating biological form was proposed. The main purpose of this chapter is to discuss experimental research that studies the relationship between UPE and morphogenetic aspects, either in development, or stress induced changes followed by recovery processes. Within that context, different biological systems will be briefly described. In plant research, the emphasis is on the relationship between spontaneous UPE and development of seedlings. In the section on UPE recordings in animals, we focus the description on the relation between UPE and cancer development. In research using cell systems, the relationship between UPE and cultured tumor cells, with different degree of differentiation is discussed. This provides information on the coupling of UPE to biological structure and the altered growth properties of tumor cells compared to normal cells. In the last part of the chapter the focus is on studies in human biology, in particular in relation to disease and (healthy) lifestyle. Correspondence/Reprint request: Dr. Eduard Van Wijk, Sino-Dutch Centre for Preventive and Personalized Medicine, Leiden University, Leiden, The Netherlands. Email: [email protected]

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Eduard Van Wijk et al.

1. Introduction The assumption of (light) radiation from biological systems 100 years ago was not completely unexpected. It was, at that time, part of mainstream developments in biology. Biology, as an independent science, was focusing at the “program for the formation of the form” (Driesch, 1911). This program is the core of several disciplines: developmental biology, regeneration biology, and tumor biology. They demonstrate the process of unfolding the form, the restoration of the form after severe damage, and the malfunctioning of the program, respectively. The “program” is more than a blueprint for the ultimate goal; it also includes the instructions for using the blueprint. With respect to the instructions to renew the body, one immediately recognizes the importance of the “program” for understanding disease and the healing process. It was the discussion on the organization of the living state’s form that led in the early 1900’s to the question where to look for the essence of the “program”: at the matter or at the carrier of the matter. In Driesch’s view, based on early systems theory, the carrier of the matter did not correspond with a purely biochemical approach. Something was lacking in the biochemical approach. Metabolism was about energy and rates. Positions (and the form aspects) were addressed with scalars, vectors and boundaries. These forces are not properties of the molecules themselves. This mystery was increasingly recognized within the framework of living systems theory that accepted the fact that a field of organization with properties specific to the totality of the form cannot be explained solely on molecular grounds (system’s particles) (Driesch, 1911). In searching for another, non-mechanical (non-particle) nature of the “program” that determines the formation of the form, an alternative regulatory principle evolved: a morphogenetic (universal biological light) radiation field, the predecessor of the biophoton field. Alexander Gawrilowich Gurwitsch (1912) considered a radiation process as a possible non-mechanical deterministic principle (Gurwitsch, 1912). In his 1912 paper “Die Vererbung als Verwirklichungsvorgang (“Heredity as a Process of Realization”), the notion of the field as applied to biological morphogenesis was defined as elements simultaneously being subject to a single morphogenetic factor. This notion was opposite to the alternative conception that considered the morphogenesis as a result of interactions between elements. According to Gurwitsch’s morphogenetic field theory, behaviour of both individual cells and rudimentary organs is controlled by a field of forces common to all elements of an embryo. It is presently understood that the field regulates the behaviour of individual cells in developing and regenerating (healing) organisms, routes their movements, controls their division and differentiation, and evolves itself with growth. Initially, Gurwitsch was cautious not specifying the physical nature and initial sources of the field but finally – in 1923 – performed “the”

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crucial onion experiment demonstrating the role of UV radiation in mitosis (Gurwitsch, 1923). It took a long time, until the early 1960’s, before Russian researchers were able to detect and confirm – without any doubt – ultra-weak photon emission (UPE) from biological systems utilizing sensitive photomultiplier equipment. It took until the early 1970’s, outside the USSR, to study ultraweak photon emission from biological organisms. The research finally began providing experimental data from three groups spread over the world, namely in Japan (Inaba), Australia (Quickenden) and Poland (Slawinski) and later being followed by teams in Germany (Popp) and the USA (Chance). In 1980, this resulted in a worldwide growth of research addressing ultra-weak photon emission. This does not mean that the 1912 proposal of a morphogenetic radiation field for regulating the biological form was accepted. In fact, the “informative” aspect of the radiation field in the original idea was forgotten. This was probably due to the long duration of physical device development as well as the predominantly biochemical interest of researchers. Therefore, at the end of these developments, the main question was whether this radiation plays a field organizational role, setting into motion development as well as disturbances such as disease and concomitant healing or malfunctioning in tumor formation. A first step in finding evidence is to study the relationship between the ultra-weak photon emission and morphogenetic aspects, either in development, or stress-induced changes followed by recovery processes, or in tumor development. The purpose of this chapter is essentially the search for these relationships. In this line, this chapter brings together pertinent examples from plant, animal and human biology. However, first, the question is asked how to characterize the photon emission properties of the living organism.

2. The photon storage phenomenon Since the time that Presman published his work on “Electromagnetic Fields and Life” (1970), it became evident that biological systems not only emit electromagnetic waves but also respond to radiation from extremely slow fluctuations up to the extremely rapid short waves in the UV region (Presman, 1970). In 1974, Cilento postulated the existence of a new type of coupled reaction in biology. The postulate was based on simultaneous occurrence of biochemiluminescence and biophotochemical reactions as well as the occurrence of excited electronic states in dark biological processes (photobiochemistry without light) (Cilento, 1974). Effective intracellular photon trapping is expected to influence metabolic and cellular events (Cilento, 1982). It is strongly analogous to the coupling of reactions that produce and utilize chemical compounds such as ATP or NAD(H) in metabolic pathways using efficient key enzyme regulations. In the course of this energy flow,

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numerous chemical reactions of diverse type take place. For different states of organization, this should indicate not only a shift in energy flow patterns, but also in the coupling of biochemiluminescence and biophotochemical reactions. Following the above view, the working hypothesis was built that measuring spontaneous emission of photons provides information about the organism’s photon field properties (e.g., photon count distribution) and that the actual number of photons trapped is additional information presumably about the physiological status of an organism. Since the capacity of cells to store photon energy is an intrinsic part of the above hypothesis, it was, furthermore, suggested to fill up the (presumed) photon stores by artificially illuminating the cells. Suppose that photons are trapped, the properties of the trapping mechanism(s) are reflected by the delayed emission. Precise analysis of the post irradiation photon emission should demonstrate the photon storage characteristics of the biological system. Especially for this latter method of measuring so-called delayed luminescence (DL), i.e. for the registration of light-induced photon emission, a double shutter system is needed, which was initially constructed by Ruth in the late 1970 in the laboratory of the Popp group (Ruth, 1979). It works such as that after excitation, a shutter between light source and sample is rapidly closed, whereas, a second shutter between sample and the photomultiplier tube is almost immediately opened. The time between closing the first and opening the second shutter was usually about 100 milliseconds (ms), meaning that recording of delayed photon emission begins 100 ms after the end of illumination.

3. Biophoton emission from plants The relationship between spontaneous ultra-weak photon emission (UPE) and development in plants has been studied utilizing seedlings of barley (Hordeum vulgare L.). As early stages of seedling growth do not need light (most seedlings can grow in darkness inside soil for days), studies on seedlings can reveal changes in photon emission properties during development. The UPE of seedlings during germination has been recorded either as two-dimensional images (Kobayashi et al., 1997; Bao, 1998) or as curves of intensity estimated with photomultiplier tubes (PMT) (Chen et al., 2003; Yan, 2005), together delivering complementary results. While a twodimensional image is able to give spatial information and emission intensity distributions of the subject under study, our interest was rather on the evaluation of temporal aspects of photon emission for which the use of PMT is the proper method. Figure 1 shows the temporal UPE (i.e., spontaneous photon emission) pattern registered by PMT of 10 barley seeds during germination.


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Immediately after the addition of water to seeds, the photon emission was rather high and then dropped fast in the first half day. Then, the emission turned to increase in the second half of the first day. After that, the UPE rises almost continuously till the end of the experiment because of the exhaustion of the available water for the seedlings in the closed dark chamber. It is interesting to see that there are several peaks in the rising UPE curve of the growing seedlings. The time difference between two of successive peaks is about 1 day, suggesting that even in a dark environment, the circadian rhythm exists in seedlings (see arrows in figure 1). The decreasing emission in the first half-day was observed in living seeds as well as in dead seeds (Yan, 2005). It corresponds with chemical or physical changes of the dry nutrients (starch, protein) during their interactions with water. The following increase in UPE, however, corresponds with growth of seedlings and is linked to cell metabolism. This has been demonstrated by sequentially blowing oxygen and CO2 gas into the dark chamber where the seedlings were growing. The UPE start dropping immediately when the CO2 influx begins.

Figure 1. The UPE of 10 successfully germinating barley seeds. The 10 barley seeds were placed in the dark chamber after the addition of 1ml (distilled) water where they began to grow. The UPE (in counts per min; Y-axis) of the seeds and the seedlings in the dark chamber was registered continuously. The arrows indicate peaks in the rising UPE curve suggesting a circadian rhythm. The figure was taken from the dissertation of Y. Yan (2002); copyright Y. Yan.

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When the CO2 influx is stopped and air is blown into the chamber, the UPE jumps high. The data led to two conclusions: (1) they demonstrate that photon emission intensity is related to development, and (2) that oxygen is necessary for photon emission. In the process of plant development, the appearance of green leaves is associated with extremely high efficiency in photochemistry based on the properties of chlorophyll. Similarly, green plant tissues have been also recognized as emitters after radiation. They show intensive, decreasing, but long-lasting UPE in studies wherein these organisms (after illumination) were placed in the dark chamber in front of a photomultiplier. This lightinduced photon emission of green plants was first reported in 1951 (Strehler and Arnold, 1951). In fresh leaves as compared to some days old leaves, the decay commonly shows a particular pattern (Figure 2a): a drop in emissions in the first minute, then a rise for minutes long to a relative maximum and after that a drop again until it reaches a relative constant level (Bertsch and Azzi, 1965; Schmidt and Senger, 1987; Yan et al., 2005). This pattern is assumed to be related to physiology because in leaves measured three days after harvest, the amount of photons emitted at the very beginning was already much lower and, further, the UPE curve showed a continuous decaying without any trace of a relative maximum (Figure 2b). In another study the relation between photon emission and the intactness of the biological structure was assessed comparing emission patterns (after illumination) from a whole leaf with patterns of homogenates of leaves, isolated chloroplasts and filtered homogenates of leaves. a)

b)

Figure 2. The induced photon emission patterns of a leaf after the illumination by 780nm LED: a) fresh leaf demonstrating an early drop in emission, then a rise to a relative maximum and after that a drop again. b) three days after harvest when the relative maximum had disappeared completely. Note that the X- and Y axis represents a logarithmic time scale. The UPE on the Y-axis is presented in counts per 0.1 s. The figure was taken from the dissertation of Y. Yan (2002); copyright Y. Yan.


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These data showed that in whole leaves a characteristic emission with the above mentioned oscillation,( i.e. the temporal minimum and maximum) was observed, while homogenates showed a decay curve without oscillation. Even though isolated chloroplasts display delayed luminescence, they do so without oscillation. A filtered homogenate shows no more light induced delayed luminescence at all, although it contains a lot of chlorophyll and other molecular components of chloroplasts. Together, these findings demonstrate a relationship between DL oscillation and the integrity of leaf tissue suggesting that the emission originates mainly from the photosystems (PS) in the thylakoid membrane of the chloroplasts (Schmidt and Senger, 1987; Yan et al., 2005; Hideg et al., 1991). Such data are the basis for the development of UPE markers to estimate the integrity and probably the quality of the plant.

4. Biophoton emission recordings in animals The body of an animal or human operates as a society whose individual members are cells. The cells reproduce by dividing themselves (cell division), while the anatomical organization of the adult body remains stable over time. In this society, each cell behaves in a social manner: dividing, differentiating, or dying as needed for the good of the organism. In contrast, cancer does not obey these boundary laws of cells in healthy tissues. Cancer cells encroach imperialistically upon neighbor cells weakening the organism the more they grow. Hence, the major interest in tumor development deals with two aspects: the tumor itself and the influence of the tumor on its bearer. In particular the latter aspect is strongly related to tumor growth and stress upon the organism. The early interest in animal spontaneous photon emission has therefore focused on organs of tumor-bearing animals. Do tumor-bearing animals show increased UPE in other parts than just the tumor of the organism? In a series of well controlled studies several types of tumor cells (Ehrlich ascites tumor cells, fibrosarcoma cells and adenocarcinoma cells) were injected in mice at locations (subcutaneous or intraperitoneal) at a distance from liver (Boveris et al., 1985). The data gave evidence that spontaneous mouse liver UPE was increased in the early phase after injection of tumor cells compared to controls. The researchers concluded that the liver of tumor-bearing animals is subjected, during the early phase after tumor transplantation, to an oxidative stress with increased levels of peroxyl radicals, which are responsible for the increased UPE in vivo. Another study (Inaba et al., 1982) described an increase UPE in blood samples from humans with carcinomas. In a study using two-dimensional imaging and photon counting of ultra-weak light emission from transplanted bladder cancer, attention was focused on the comparison of the cancer with the surrounding tissue

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when the bladder cancer was transplanted into the feet of nude mice (Amano et al., 1990; 1995). By utilizing two-dimensional imaging, the growing cancer can be specifically distinguished: increased photon emission was observed in the implanted tumor region (Amano et al., 1995). In the early stages of development, tumors gradually disrupt the organizational stability, thereby disrupting normal function of the afflicted tissue. In this early phase changes in interactions occur prior to necrosis, hemorrhage, leukocyte infiltration or crusta formation. It suggested that increased emission corresponded to the increased division activity of the tumor cells. Other studies confirmed that, ultra-weak photon intensity from different transplanted malignant tumors was distinctly higher than was recorded for normal tissue (Amano et al., 1995; Kim et al., 2005; Shimizu et al., 1973). More recently, ultra-weak photon detection was reported from tumors transplanted in mice utilizing a highly sensitive and ultra-low-noise charge-coupled device (CCD) camera system. In addition, a procedure for whole body scanning of mice was developed utilizing a small, mobile and sensitive photomultiplier tube (PMT) operated at room temperature in a dark box (Takeda et al., 2004). These investigations focused on mice that were transplanted with ovarian cancer cells. All data confirmed the increased photon emission in disregulated tumorbearing tissues. It leads to the conclusion that not only cells with a high division potential demonstrate increased photon emission, but equally well that this detection method may be developed into a diagnostic tool. In animals, the question has also been asked whether ultra-weak photon emission is oxygen dependent. For tumor-bearing tissues, such study is not available. However, an answer comes from two-dimensional imaging of the biophoton emission from a rat's brain, detected in vivo over the skull. Kobayashi and colleagues (Kobayashi et al., 1999; Kobayashi, 2005) demonstrated that brain photon emission was strongly decreased in the absence of O2, i.e., under ischemic conditions. Additionally, they simultaneously measured photon emission and electroencephalographic (EEG) activity, demonstrating a correlation between photon emission intensity and the theta wave component of the EEG power spectra. The decrease of photon emission in ischemia makes the interpretation of the tumor observations more complicated. Thus, it is well known that tumor cells have a decreased respiration and instead an increased fermentation (Weber, 1983). It may be suggested that tumors are detectable in early stages only, but this needs further study.


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5. Photon emission from isolated tumor cells Similar to plant tissues and cells, the animal cells also demonstrate the property of a decreasing UPE when recording starts immediately after illumination. This allows the measurement of intracellular photon trapping and its relationship with alterations in metabolic and organizational states. This relationship was studied using cultured tumor cells with different degree of differentiation. It can give information about the coupling of biophoton emission to biological structure and the altered growth properties of tumor cells compared to normal cells. The studies began with the establishment of the molecular correlation concept in “in vitro” cultured hepatoma cell lines in the early 1970’s by Van Wijk and colleagues. The molecular correlation concept is one of the major concepts in cancer research (Weber and Lea, 1967). Using many transplantable hepatomas (tumors of the liver) it was shown that the biochemical parameters of alterations were the result of a reprogramming of gene expression that was both qualitative and quantitative. It made it possible to pinpoint the reprogramming of gene expression conferred to cancer cells, including the strict reverse relationship between differentiated functions and growth rate. A study of the molecular correlation concept for in vitro liver and hepatoma cells could provide a model system of different states of tumor development that could then be used to study photon emission characteristics. Four hepatoma cell lines isolated in the late 1960’s were used for comparative biochemical studies. Those cell lines are commonly named H35 (Pitot et al., 1964), HTC (Thompson et al., 1966), MH1C1 (Richardson et al., 1969) and RLC (Gerschenson et al., 1970; Oshiro et al., 1972). They were studied for their degree of differentiation as systematically evaluated at the level of cell morphology and functioning, including sensitivity for hormonal regulation (Van Wijk et al., 1972a; 1972b; 1972c; 1974), ultrastructure of the cytoplasm, mitochondrial volume and structure (Volman, 1978; Volman and Van Wijk, 1980), glycolytic and gluconeogenic functions and (iso-) enzyme activities (Schamhart et al., 1979), growth and division potential (Van Wijk et al., 1973; Wicks et al., 1973a; 1973b). The series of rat hepatoma cell lines definitively illustrated the “molecular correlation concept” at the level of cells “in vitro“. The liver characteristics partially persisted in a coordinated manner in the welldifferentiated hepatoma cells H35 and MH1C1, whereas in the poorly differentiated cell lines HTC and RLC the differentiation characteristics were lost. These hepatoma cell types and the liver parental cell, thus, were used to study the relationship between the state of differentiation and the photon storage capacity. In 1983, a long-term cooperation between the research groups of Popp (Kaiserslautern, Germany) and Van Wijk (Utrecht, The Netherlands) began. Van Wijk and colleagues used three types of rat liver

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cells: from fully differentiated rat liver cells to very poorly differentiated (HTC) rat hepatoma cells with well-differentiated (H35) rat hepatoma cells as an example of an intermediate state differentiation. The comparative studies, suggested that light-induced delayed photon emission (reflecting the photon storage capacity) differed between cell types (Schamhart and Van Wijk, 1987; Van Wijk and Schamhart, 1988; Van Wijk et al., 1990; Van Wijk and Van Aken, 1991; 1992). To summarize the data: liver cells re-emit low numbers of photons at a slow rate, while poorly-differentiated HTC hepatoma cells re-emit high number of photons with a fast speed. The welldifferentiated H35 hepatoma cells have intermediate properties. The data correspond with the view that the coupling of biochemiluminescence and biophotochemical reactions fits the molecular correlation concept of tumor development. The data also confirm the photon storage model as proposed by Popp: According to this model (which was initially described in terms of a resonator cavity), a high quality resonant system loses only a small amount of its energy (photons) per unit time, while a low quality system (due to the malfunction of the feedbacks) will give a larger response to the (light) stimulus (Nagl and Popp 1983; Popp et al., 1981).

6. Biophoton emission recording in humans The last example of studies on the relationship between the ultra-weak photon emission and morphogenetic aspects focuses on studies in human biology, in particular in relation to disease and (healthy) lifestyle. First of all this requires the measurement of human body biophoton emission. Secondly, it requires studies in which human subjects have been distinguished into well-defined life styles or diseases. In the 1970’s, human biophoton emission research was primarily focused on photon emission of specific human body fluids. Research on human body photon emission started in 1979 (Dobrin et al., 1979). A special project on biophoton emission, the “Inaba Biophoton Project” started in Japan in 1986. The project was funded by the Exploratory Research for Advanced Technology (ERATO), a subsidiary of the Research Development Corporation of Japan (presently, Japan Science and Technology Corporation). Human photon emission was hypothesised to reflect the physiology of the human being. This led to medical diagnostic research utilizing biophoton emission (Inaba, 1988; Swinbanks, 1986). Several studies suggest that the intensity of photon emission changes in a state of disease. Japanese studies of the two-dimensional pattern from the index and middle finger indicated that intensities could be used to differentiate hypothyroidism, a lower state of metabolic activity (Usa et al., 1991; 1994; Usa and Inaba, 1995). Ultra-weak photon emission in patients with hyperthyroidism was less intense than normal. The lower emission was also found in patients whose thyroid glands


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had been removed. In Germany, Popp and co-workers pioneered since 1993 in human biophoton emission research, utilizing a cooled system with a detector head hanging from rails that could be positioned over any part of a subject. The device was utilized to record emission from 80 healthy and diseased subjects over various body areas. The study confirmed differences in emission between subjects as well as between body locations (Cohen and Popp, 1997a). However, only a few anatomic sites were recorded for each subject and a systematic measurement schedule was not followed. Another study reported on several multiple sclerosis patients who emitted more photons than ordinary healthy subjects (Cohen and Popp, 1997a; 1997b; 2003). In this study, the authors introduced a second parameter for disease, e.g., percentage of difference in emission between left and right hand. They suggested that in certain diseases left-right symmetry was broken. In a South Korean study, left-right symmetry of photon emission from the palm and the dorsum of the hands of hemiparesis patients were compared with similar data from the hands of healthy subjects. The variation in left-right symmetry among healthy subjects was not large. In hemiparesis patients though, the left and right differences were reported very large in both for the palm and dorsum of the hand (Jung et al., 2003). To initiate systematic body research for the research on the relationship between stress and photon emission in humans, Van Wijk and Van Wijk (2004; 2005a) described a protocol for multi-site recording of subjects. Anatomic sites were selected such that the distribution in photon emission could be studied as right-left symmetry, dorsal-ventral symmetry, and the ratio between the central part of the body and extremities. Although data again demonstrated the variability in patterns between subjects, some generic features were observed: a) the overall intensity of photon counts over the body was lower in the morning than in the afternoon, b) the thorax-abdomen region emits the lowest but most constant emission, and c) the upper extremities and the head region emit the highest levels and increase during the day. The data suggested that a “common” human biophoton emission pattern exists in addition to individual emission patterns and dynamics. This spatial distribution of human ultra-weak photon emission and its dynamics was confirmed by utilizing a highly sensitive charge-coupled device (CCD) imaging system (Kobayashi, 2003; 2005; Van Wijk et al., 2006a; 2006b; Kobayashi et al., 2009). The mounting evidence indicating that human photon emission can be reliably measured and may be different in some human pathology (Van Wijk et al., 2008) led to an increased interest in the ultra-weak photon emission in relation to lifestyle. It is generally accepted that meditation, if practiced for a long time, induces a greater state of self-awareness and inner calm in its practitioners. The resulting reduction of stress may have prophylactic and therapeutic health benefits. The hypothesis suggesting a

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possible link between meditation and its therapeutic effect had stimulated considerable curiosity in the scientific community. The measurement of serum lipid peroxide levels in plasma indicated lower lipid peroxide levels in practitioners of meditation (Schneider et al., 1998; Kim et al., 2005; Yadav et al., 2005). Ultra-weak photon emission studies focused on longterm practitioners of meditation. The first studies utilized the multi-site recording system (Van Wijk and Van Wijk, 2005a). The comparison between practitioners of meditation and subjects without experience in meditation indicated intensity discrimination in emission (Van Wijk et al., 2006). A follow up study examined the ultra-weak photon emission from the hands of three groups of subjects: control group having no experience in meditation, TM group practicing Transcendental meditation, and a different group practicing a form of meditation other than TM (OTM). Each group consisted of 20 healthy, non-smoking subjects. Data demonstrated that the intensity of ultra-weak photon emission by subjects of both meditation groups is lower by 15–33% for the TM group and 4–15% for the OTM group compared to the control group. All subjects demonstrated a high degree of symmetry (Van Wijk et al., 2008a). Additionally, the photon signal was described according to a quantum optical approach utilizing four parameters (|α|, φ, r, θ) that determine the signal (Van Wijk et al., 2008b). Both the squeezed state parameters and asymmetries suggest that the control group is different from both meditation groups. The difference between TM and control group is more than that between OTM and control group. The data support the conclusion that reduced stress experience in human subjects does correspond with a change in photon emission parameters. Like in other studies on spontaneous photon emission, attention was paid to the role of oxygen in human photon emission. In different types of experiments the effect of hypoxia on photon emission was investigated. A tourniquet was placed around the upper arm to depress the supply of oxygen and nutrients to the hand. Photon emission of the hand was recorded during periods of increasing degree of tourniquet tightness. Data demonstrate that photon emission progressively decreased during blood flow limitation (Yang et al., 2004; Van Wijk and Van Wijk, 2005b; Scholkmann et al., 2013). Direct exposure of the hand to oxygen deprivation also resulted in some decrease of photon emission (Nakamura and Hiramatsu, 2005). Apparently, the generation of photons emitted from the hand is due to both, interior sources and the skin itself.


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7. Conclusion The examples presented in this chapter cover a multitude of situations and organisms demonstrating that the organization of the living state corresponds with photon emission characteristics of the organism. Examples are found in plant, animal and human biology, but also in cell biology. Even though this research can be qualified as correlative research, many of the results gave evidence for implicit hypothesis as there was the parallelism between physiological reactions and UPE or the light store capacity in biological material. Such research, however, is of large importance in approaching the morphogenetic field theory. According to this field theory, the behaviour of both, the individual cells and recovery (or repair) processes in healing are controlled by the field. Presently, it is the task to find at the organismal level a model in which an unbalanced condition can be healed by interfering exclusively with the biophoton field. We have suggested that such interference may be brought about by extremely low light therapy procedures (Tafur et al., 2010). It is without doubt that Low Intensity Laser Therapy (LILT) is able to result in wound healing etc. Our studies resulted in the development of procedures to analyze the photon signal. This led to the hypothesis that the analysis of the “language” in the emitted photon signal has information about the state of health and disease.

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Chapter 8

Electromagnetic cell communication and the barrier method Daniel Fels Institute of Botany, University of Basel, Switzerland Abstract: Cells emit electromagnetic signals. To focus on the understanding of the function of these signals, they need to be investigated by separating them from chemical signals. This is achieved with barriers disabling a transmission of chemical but not electromagnetic signals. Hence, the barrier method is described and examples of experiments are given that allow deducing a function of the signal (or not). Furthermore, confounding factors such as chemicals or room light are discussed. An approach towards non-invasive technologies is deduced from proposed experiments. Finally, it is concluded that the examples of electromagnetic cell signals that induce cell processes strongly support the basic hypothesis of an interaction between the fields and the molecules of the cell. Correspondence/Reprint request: Dr. Daniel Fels, Institute of Botany, University of Basel, Switzerland E-mail: [email protected]

1. Introduction Communication demands a transfer of signals from a sender to a receiver. Since the receiver interprets the signal, the combination of both sender and receiver determines a function that is attributable to the signal. Hence, when we want to understand electromagnetic cell communication we have to know about the signal and the function it induces. Yet, when we measure electromagnetic waves released from cells with a technical device such as a photomultiplier, we will not know whether these waves induce functions in other cells. Similarly, when we find effects in cells (presumably signalinduced functions) that are separated with glass barriers from inducer cells, we do not know about the source of the signal within the cell. This problem has been known since the very beginning of the research on electromagnetic

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cell communication. While a function could clearly be ascribed, namely induced cell division, the emitting source within the inducing cells leading to socalled mitogenetic radiation (Gurwitsch, 1923) was unknown. However, today we know that emissions from cells cover the whole spectrum from low energetic radio waves to high energetic UV-waves (Cifra et al., 2011). We also gained more understanding about the molecular sources of these broad range of electromagnetic frequencies (Cifra et al., 2011, van Wijk, 2001) with suggested sources for UV emission (Voeikov, 2011, Voeikov & Beloussov, 2007) not being commonly accepted. We are at the beginning of learning about structures perceiving and translating these signals into functions (Tsong, 1989) - an endeavour that needs the unified strength of both molecular and “electromagnetic” biologists, and may lead to the discovery of hitherto unknown photoreceptors, a process that has begun already (Briggs & Spudich, 2005, Idnurm & Crosson, 2009), probably including cell water itself (Chai et al., 2009, Pollack, 2012). Yet, independent from the signal sending and receiving sources, it is assumed that the visible range is a very efficient frequency window for electromagnetic cell communication. One reason is that the thermal electromagnetic noise which is omnipresent due to surrounding temperature (Planck’s law) in those ranges commonly present on Earth has a low intensity in the visible and UV range, and so the signal-to-noise ratio can be high in that range. The other reason is that the energy content of photons in the visible range is high enough to trigger chemical reactions and, thus, can finally lead to chemical cascades and functions, respectively, as in photosynthesis or vision. Apart from the uncertainty about the sending and receiving structures within cells (Trushin, 2004a), let alone the assessment of these structures while cells communicate, we simply state that cells, as complete units of life, function as both sender and receiver. As we focus here on functions, we will look mainly at the receiver. This is based on the assumption that the effects are induced by electromagnetic waves and hence, that they can be understood as functions. Furthermore, it follows the basic assumption (hypothesis) that cells are able to generate and perceive electromagnetic fields simultaneously.

2. Signal selective barriers In order to test for effects due to presumed electromagnetic cell radiation in a biological system that produces metabolites potentially functioning as chemical signals, one has to isolate the electromagnetic from the chemical signals. Note, sound has been postulated as a physical signal, too, but mainly for bacteria and not as an omnipresent phenomenon (Reguera, 2011, Scholkmann et al., 2013); the researcher should bear in mind when performing experiments with barriers that sound can tresspass them.

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As chemicals are an omnipresent confounding factor (Trushin, 2004a), one possibility is to control for them by using barriers that allow the transmission of electromagnetic waves but not of chemicals. Interestingly, in experiments on chemical cell-to-cell signalling, electromagnetic signals must be seen as a confounding factor, too. Therefore, in order to get a more complete vision of cell communication, we have to understand the simultaneous effects of chemical and electromagnetic signals. However, this is an endeavour that can only be undertaken when we start understanding more about the function of electromagnetic cell signals. Barriers needed to isolate electromagnetic from chemical signals traditionally consist of quartz glass, allowing the transmission of long wave frequencies up to UV-C (Gurwitsch, 1923, Gurwitsch, 1926) with an equal absorption spectrum from microwaves to UV-C (Fels, 2009). Normal glass (but of purest quality) gives a similar spectrum except for cutting higher energy radiation such as UV-B and UV-C (Fels, 2009). In contrast, transmission spectra from any type of plastic material are very inhomogeneous (see spectra on websites for plastic cuvettes). Due to this, results from experiments with plastic barriers might produce a distorted reflection of the nature of the phenomenon. Hence, the use of pure SiO2-based barriers is recommended because of its homogenous transmissibility of electromagnetic signals. Two populations can be separated either vertically or horizontally depending on the biological material and the barriers used (Fig 1). With petri dishes one rather separates cultures horizontally (Rossi et al., 2011), while working with moving cells in cuvettes, these may be placed side-by-side (Fels, 2009). Some develop their own barrier-system (Farhadi et al., 2007). Interestingly, the literature describes so far only the separation of two populations of cells or tissues with barriers, while separating more than two populations, we could artificially create a situation that resembles embryonic development where different cell types need to be coordinated.

3. The fingerprint problem Even though photon density within cells is assumed to be very high (the radiant flux is approximated to be in the range of 10-16–10-7 W/cm2) (Bokkon et al., 2010), we know that photon emission of cells is ultra-weak (Cifra et al., 2011). Yet, the first experiments on electromagnetic cell signalling were performed under room-light conditions where the amount of photons per cm2sec exceeds the number of photons emitted by the communicating cells billion-fold.

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Figure 1. Cuvettes and their use. (a) Two cuvettes of different size and height with the smaller one standing in the bigger one; (b) from top and (a) from the side show dimensions of cuvettes as used in Fels (2009); in (c) we see the use of such an abovementioned pair of cuvettes for a side-by-side separation with a maximum of exchange surface between an outer cell population (or tissue) B on an inner cell population (or tissue) A; in (d) the cuvettes are positioned in such a way as to have signal exchange in a vertical direction, too, e.g., for testing effects among sinking biomaterial (yeast cells, or fish eggs); in (e) the contact surface between A and B is smaller than in (c) but this side-by-side positioning allows to test for distant effects or allows the use of several cuvettes standing in contact.

Recall that daylight consists of UV-A and -B while -C is filtered by the ozone layer (Letokhov & Dobryakov, 2003) and the whole range of visible photons (as well as lower energy waves). Since there are photoreceptors in organisms sensitive to photons from such natural or artificial (chaotic) light


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sources we have to ask, how does a cell distinguishes between the few photons from a neighbour cell population and the tremendous number of photons from the sun (or light bulb)? This non-trivial problem (read Kucera & Cifra, 2013) has also led to the assumption that cell-photons are emitted from a non-chaotic source that gives the photons emitted by cells a physical fingerprint. Most prominent became the theory of coherently emitted photons (Popp, 2006, Popp & Yan, 2002). However, this theory on coherent cell radiation is controversially discussed (Budagovsky et al., 2007, Salari & Brouder, 2011), and we conclude that the fingerprint-problem is not yet solved. With respect to the light-conditions when performing a barrier experiment, we note that regularly in the past but also in recent papers (Jaffe, 2005), authors often did not mention whether it was performed under darkness or room-light conditions: it is, therefore, not possible to list them desirably with regard to the results (Cifra et al., 2011). Yet, we can assume that early experiments were performed under conditions of room light because they were inspired by the pioneering experiment done by the Russian morphologist A.G. Gurwitsch (1874–1954), which he had performed under conditions of room-light: Gurwitsch assumed (even though not exclusively) that light dependent reactions were the sources of the emitted radiation (Gurwitsch, 1988, Voeikov & Beloussov, 2007). In order to separate chemical from electromagnetic signals and simultaneously get out of the fingerprint problem it is suggested to separate cell populations under conditions of total darkness. Whatever the result will be, it will not depend on (nor be influenced by) room light - it will refer to the neighbouring cell population only. Such a one-factorial design is therefore a required condition when isolating electromagnetic waves from cells in order to understand the function they can induce. To be more accurate, keeping two mutually exposed but chemically separated cell populations in a black box leads only to quasi-total darkness since it is dark for visible light but not with regard to the thermal radiation of the material surrounding the cells. Nonetheless, controls do exclude these infrared waves as cause for observed effects. The black box bears also an additional component. Some cells being part of a tissue (but also being, e.g., a parasite) somewhere “in the depth” of a multi-cellular organism may indeed experience such quasi-total darkness. Yet, for singlecell free-living aquatic organisms such a condition, namely quasi-total darkness is generally not met in nature. Hence, when performing experiments under conditions of quasi-total darkness and finding cellular functions, we look at a between-cell relationship that may have resulted from natural selection regarding cells “from the depth” of multi-cellular organisms. For free-living single-cell organisms, however, these relational patterns may not have resulted from natural selection.

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4. Multitude of effects The discovery of mitogenetic radiation by A.G. Gurwitsch delivered the scientific community with strong evidence for a non-chemical causation of the most fundamental life process: cell division (Gurwitsch, 1923). Yet, at the beginning of the last century, hormones were already discovered and together with the integration of the key-lock principle tremendously supported the investigation of chemical signalling, despite the assumption of Gurwitsch (1923) that mitogenetic radiation is hierarchically seen “above” chemicals. Gurwitsch and his group found evidence for cell radiation with upregulating effects: In his famous onion root experiments (1923), which was repeated by many others (e.g., Reiter & Gabor, 1928), he found a significant increase in mitosis in the root meristem of the receiving root exactly there where that root was exposed to the tip of an inducer root. Gurwitsch, as a morphologist being interested in the appearance of form, was certainly encouraged to have found a non-molecular up-regulating factor. Even though the appearance of form demands also down-regulation (e.g., when an organ or an organism has reached its final size, or when fingers appear in a developing embryo due to apoptosis), the Gurwitsch group did not test for downregulation. They investigated the question of the cause of the signal and used general field theory to explain morphogenesis (Beloussov, 1997). Further, experiments across the species border (Reiter & Gabor, 1928) were performed testing for the generality of the phenomenon (for a review, see Cifra 2011). With the development of photomultipliers (in the early 1950ies), measuring the emission of electromagnetic waves from biological material in the visible range (Colli et al., 1955, Strehler & Arnold, 1951) mitogenetic radiation lost its adjective mitogenetic and new terms appeared like ultra-weak photon emission (UPE) or biophotons (Niggli, 1992, Popp, 1988). For a while, machines became the detectors of this radiation and not life anymore: The signal characteristics became the focus and not their biological significance (function). From the 1980’s on, organisms were again taken as detectors for non-chemical signal transfer, unfortunately (and ironically) often not controlling for confounding effects from chemicals (Trushin, 2004b). Two interesting studies will illustrate that problem. One refers to the dependence of photon emission of the crustacean Daphnia magna, on its artificially increased density (Galle et al., 1991). There was an overall but non-linear increase in photon emission assessed: the minima and maxima indicated a density-dependent cause for maximal or minimal absorption of photons by the releasing crustaceans themselves. This supported the assumption of photonbased communication with (spatially) constructive and destructive interference (Galle et al., 1991, Popp & Klimek, 2007). However, the organisms


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were all in the same cuvette and a density dependent release of chemicals may have confounded photon emission disabling a proper interpretation of the communication system. Similarly, in a study with malignant and healthy cells, the isolated malignant cells displayed an exponential increase in light induced (i.e. non-spontaneous) photon emission with increasing density, while the healthy cells displayed a decrease in (light induced) photon emission when measured at increasingly higher densities (Schamhart & van Wijk, 1987). This indicated, at a first look, again a photon-based communication with constructive and destructive interference (Popp & Klimek, 2007), but the differing densities may have correlated with differing releases of chemicals that contributed accordingly to the emission of photons. Hence, in order to distinguish between chemicals or photons organizing communication, both studies (Galle et al., 1991, Schamhart & van Wijk, 1987) would need a continuation with barriers in use. As such, we learn from both these experiments that density of cells or of crustaceans, respectively, correlate with photon emission, but we cannot deduce a function induced by the photons. A wonderful experiment indicating communication between chemically isolated populations comes from the protozoan Gonyaulax polyedra (phylum: Dinoflagellata) showing the release of irregular bursts of photons of the same frequency (note that we talk here of bioluminescence) (Popp et al., 1994). The study demonstrated that these bursts were caused by a communication system between the cells working across glass barriers. When two populations were placed side-by-side in cuvettes with a photon shield between them, the bursts of each population were asynchronous in comparison with the other population. When the shield was removed, however, the bursts of the two populations became synchronized. Note that we are looking here at chemical reactions (leading to the bursts) that are induced by a signal that works across glass barriers. Even though the study does not reveal the function of synchronous light bursts, it shows the capability of a supra-cellular organisation based on an endogenous source most probably of electromagnetic nature. Many other groups demonstrated inducing chemical reactions from one population to another population under the exclusion of chemical signals by using barriers. The group working with Shen provided evidence … that a long-range optical coupling of biological significance between living cells exists (Shen et al., 1994). In particular, the addition of phorbol myristate acetate to one population of neutrophils (i.e., cells) led in the other population to an increase in (i) photon emission, and (ii) the production of superoxide radicals. These effects were absent when the two cuvettes containing the populations were shielded from each other. Galantsev and his group provided similar results in a study on effects of different physiologically active substances from mouse mammary tissues on isolated mammary cells (Galantsev et al., 1993). They report that the induction of acetylcholine or

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norepinephrine into the medium of one cell population resulted in protein synthesis in the other population from which the former was chemically but not optically separated (Galantsev et al., 1993). Farhadi’s group added H2O2 to one group of intestinal epithelial cells and found a reduction in protein synthesis in neighbour cell cultures as compared to controls (Farhadi et al., 2007). Furthermore, they report cytoskeletal changes in structure and increases in nuclear extracts; some of the effects were obtained over a distance of 4 cm (Farhadi et al., 2007). Very surprising results are reported from the Kaznacheyev group who worked with tissue cultures and various types of viruses deleterious to the tissue cultures (Kaznacheyev, 1982). They separated two cultures chemically from each other in a side-by-side arrangement allowing transmission of electromagnetic waves incubating one of the two cultures with a lethal pathogen. What they observed over many tissues and deleterious agents, in general, was that neighbour tissue cultures died as well. However, it is not clear whether we can speak of a function when the neighbour population is killed. Nonetheless, these experiments are famous and, hence, deserve to be mentioned. The question of the function is important, but being at the beginning of this research on electrodynamics in cell organisation every finding confirming that effects occur across barriers is important evidence for the phenomenon as such. For example, in a cross-species experiment on isolated human microvascular endothelial cells and mouse fibroblasts (Rossi et al., 2011), where it is not clear on how to understand the results in terms of function, there were pronounced effects: changes in morphology and growth rates of cells induced across barriers. Coming back to morphogenesis where we have cell migration besides cell division and differentiation also, two studies give evidence that cell migration and positioning might also be under electromagnetic control. The first study refers to relative cell positioning of isolated (vertebrate cells) on either side of a glass slide (Albrecht-Buehler, 1992): after a first layer of isolated cells had adhered to one side of a glass slide in a non-organized (appearing) manner, a second layer of cells was placed at the opposed side of the slide and was seen to adhere in a position perpendicular to cells of the first layer. This positioning effect disappeared when the slides were shielded for electromagnetic waves. The second study dealt with the long unanswered question of how Zygotes of Fucus sp. know where the substrate is onto which they grow when germinating. It turned out, in an experiment where substrate (a seaweed) and Zygotes were separated by a chemical barrier, that a majority of Zygotes grew towards the substrate while growing in all directions when that barrier was shielding electromagnetic waves in the optical region of the spectrum (Jaffe, 2005). Recent additional evidence for effects across glass barriers come from the Ciliate Paramecium caudatum, a freshwater unicellular organism in-


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habiting ponds of the Eurasian plate. Being familiar with this organism (Fels & Kaltz, 2006, Fels et al., 2008) a study was started focusing on cell division. The results were promising. Cell populations could have enhancing as well as decreasing effects on cell division in neighbour populations, which depended on the separating material, either normal or quartz glass, indicating that other frequencies than those in the (strong) UV range contributed also to the effect. Further, energy consumption was influenced in either way depending on the number of neighbouring cells and the separating material (Fels, 2009). Interestingly, in the latter experiment, a short exposure to room light would blur effects, indicating that Paramecium caudatum has photoreceptors and is principally able to organize itself based on the detection of photons. In fact, effects on cilia movement due to light were described for P. caudatum (Okumura, 1963) or regarding meiosis and conjugation for P. bursaria (also when free from Chlorella, a photosynthetic symbiont) (Ehret, 1953). The general sensitivity to radiation in the genus Paramecium has been manifoldly described (but not with regard to cell division) (Wichtermann, 1986) while sensitivity to ultra-weak photon emission is rarely reported (Fels, 2009, Kozlov, 2000a). Effects across barriers are described for quite different systems like plants, animals or single celled organisms. We may legitimately deduce that we are looking at a general property of life. Indeed, research with prokaryotes (non-nucleated cells, i.e. bacteria) (Nikolaev, 2000, Trushin, 2004a) confirms that these very primitive cells do also use endogenous electromagnetic signals for communication. This, however, cannot be surprising since prokaryotes have similar cell internal processes as eukaryotes (nucleated cells) and, further, are assumed to have led to the eukaryotes through a process called endosymbiosis (Margulis, 1981).

5. Environmental radiation and non-invasive cell research Electromagnetic cell radiation (even though it may differ in field characteristics) is an environmental radiation like the one from the sun or an electronic device. A recent study, e.g., shows that cells (Paramecia caudatum) responded with changes in growth and shape to the exposure of microwaves (900 MHz, 2 Watt) emitted by a switched-on cell phone (the treatment) (Cammaerts et al., 2011). Another study reports on cancer incidence that oscillates in accordance with rhythms of solar radiation (Juckett, 2009). The sensitivity of life to environmental radiation might also be expressed in ecological studies when experiments are repeated over time; the repetitions can contribute highly significantly to the variation in the data set (e.g., Fels, 2005). As the experimental set-up is standardised, the factor causing this effect from repetition is rather correlated with time than with space. It might therefore be that life fluctuates with a radiation source from the environ-

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ment, probably – and this should be seen as a working hypothesis – fluctuations in a cosmophysical and/or heliogeophysical factor, e.g. cosmic radiation (Kozlov, 2000b, Trushin, 2004b). In another study it was reported that this effect from repetition was always found in controls but only in one of two mutually (“dark”) exposed populations, namely in the population that was affected by its neighbour population (Fels, 2012). Due to its regular appearance this was interpreted as a pattern displaying a law-like property where one of the mutually exposed populations relates to the neighbour, the other to an external factor. Together they build a system and, hence, communicate (this is also presented in either of the figures 2a or 2b but serves there another purpose). With reference to electromagnetic signalling between cells, one can imagine applying to any biological material different electromagnetic frequencies, looking for effects. Similarly, one may first analyse electromagnetic cell signals, reproduce them technically and apply them. An indirect method can also be imagined (Fig 2), where we apply a particular frequency in different amplitudes and see, first, whether one of two electromagnetically coupled populations will relate its growth rates or any other typical cell process to it and, second, whether this would influence the second population in its effects on that first population (compare this with Fels, 2012). In any case, the more we understand the signal quality with respect to the induced effect the closer we are to non-invasive healing technologies.

Figure 2. A hypothetical situation. On the left side (a) biological system B is sensitive to a particular frequency (f1) and system A relates to B. On the right side (b) another frequency (f2) is applied and the relations of the same cultures A and B have changed: A is sensitive to that particular frequency (f2) and B relates to A.


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6. Conclusions The research in electromagnetic cell communication leaves us with many open questions. We still do not know (i) the extent to which chemical reactions occurring within cells can be induced electromagnetically, (ii) how more than two populations of cells or organisms act (in a barrier experiment) on or interact with each other, or (iii) to what extent cosmophysical factors contribute to life processes. But the barrier method is a useful tool to investigate electromagnetic cell signals that, in addition, are of high speed and low cost and are assumed to build unity among cells. Adding such physical understanding to cells does not compete with our chemical understanding of cell processes, it rather offers a unified understanding of the two (as in electromagnetobiology and photobiology). Such synergistic approach will inevitably lead to an enlarged understanding of life, namely that there is a chemical as well as an electromagnetic communication system at work in cells assumed to belong together (Beloussov, 2011). We recall just three studies supporting this. Oxidative processes induced by H2O2 leading to photon emission, therefore showing a connection between chemical and electromagnetic pathways (Farhadi et al., 2007). Alterations in protein content due to exposure to neighbouring cells (Galantsev et al., 1993, Shen et al., 1994) give at the same time evidence for regulatory effects on the gene level. We assume actually that the two systems, i.e. the chemical and the electromagnetic, belong together in such ways as they feedback on each other. Many experiments, including those on signal-induced cell division, strongly support the corresponding basic hypothesis that cells induce fields that feed back on cells.

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Strehler, B. L. & Arnold, W. 1951. Light production by green plants. Journal of General Physiology 34: 809–820. Trushin, M. V. 2004a. Light-mediated "conversation" among microorganisms. Microbiological Research 159: 1–10. Trushin, M. V. 2004b. Distant non-chemical communication in various biological systems. Rivista di Biologia Biology Forum 97: 399–432. Tsong, T. Y. 1989. Deciphering the language of cells. Trends in Biochemical Sciences 14: 89–92. van Wijk, R. 2001. Bio-photons and Bio-communication. Journal of Scientific Exploration 15: 183–197. Voeikov, V. V. 2011. Key role of stable nonequilibrium state of aqueous systems in bioenergetics. Russian Journal of General Chemistry 81: 41–49. Voeikov, V. V. & Beloussov, L. V. (2007) From mitogenetic rays to biophotons. In: Biophotonics and Coherent Systems in Biology, (Beloussov, L. V., Voeikov, V. V. & Martiniuk, V., eds.). pp. 1–16. Springer. Wichtermann, R. 1986. The Biology of Paramecium, 2nd ed. Plenum, New York.



Chapter 9

Coherence and statistical properties of ultra-weak photon emission 1

Christian Brouder and Michal Cifra

2

1 Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Université Pierre et Marie Curie-Paris 6, CNRS UMR7590, 4 place Jussieu, 75005 Paris, France; 2 Institute of Photonics and Electronics, The Czech Academy of Sciences, Chaberska 57, 18251 Prague 8, Czech Republic

Abstract: We present a critical review of the works related to the statistical properties of biological ultra-weak photon emission (UPE) and in particular to its coherence properties. Starting with a brief description of the concept of coherence in quantum and classical physics, we then review models used to analyze photon distributions obtained from measurements of UPE. Moreover, we review experiments focused on statistical properties of UPE and try to assess them from the point of view of current understanding of physics and biophysics. A critical study of the results and their interpretations leads us to conclude that there is no undoubted proof of the coherence of UPE. We highlight particular problems of past research with respect to data interpretation or hypothesis building when looking for coherent light sources or emission and discuss the application of standard quantum optical methods for assessing the coherence of an optical field. Since emerging studies show that not only coherence properties but also fractal and chaotic properties of UPE time series signals can be analyzed, we outline briefly these fractal and chaotic properties of UPE time series presenting them as a possible new avenue for UPE signal analysis. Correspondence/Reprint request: Dr. Michal Cifra, Institute of Photonics and Electronics, The Czech Academy of Sciences, Chaberska 57, 18251 Prague 8, Czech Republic. E-mail: [email protected]

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Christian Brouder & Michal Cifra

1. Introduction From the very beginning (1920s) of biological ultra-weak photon emission (UPE) research, scientists were wondering whether the light spontaneously emitted by biological cells exhibits any exceptional signal property. Especially with the arrival of quantum optical theory of coherence (1960s), leading for example to the development of lasers, it became obvious that light can manifest very special properties with coherence being the most remarkable one. It was hypothesized for biological systems that their internal electromagnetic field is a coherent field and, further, that this coherence plays a significant role in pattern formation of biological systems (Pokorný and Wu, 1998; Popp, 2005; Cifra, 2012), since it is well known in physics that coherent electromagnetic fields can interfere and form stable space-time patterns. Furthermore, research of statistical properties of UPE signals (not limited to coherence) is intriguing because it does not deal only with quantity (intensity) and color (energy) of the detected light but also with its quality (orderliness, informational content). Such research is basically focused on quantifying the time sequences of photons released from an optical field using various physical and mathematical methods (e.g., for quantifying photocount distribution, or for assessing fractal and chaotic properties). In addition to obtaining a new feature for getting “fingerprint” photons possibly leading to applications in diagnostics, these statistical properties of UPE signals bring also insight into the physical nature of light from biological systems and moreover, the photon generating processes therein. In the following we present (i) a brief description of the concept of coherence for so-called quantum and classical cases. We then review (ii) models used to analyze distributions of photons emitted from biological systems. We also review (iii) experiments focused on statistical properties of UPE and try to assess them from the point of view of current understanding of physics and biophysics. Finally, we outline (iv) fractal and chaotic properties of UPE time series as a possible new avenue for UPE signal analysis.

2. Coherence of light Coherence is one of the fundamental statistical properties of light and yet quite subtle. In a nutshell, coherence is the ability of light to build interference. This is (according to Grimaldi) the fact that darkness can be obtained by adding light to light1 (Grimaldi, 1665, p. 189). Broadly speaking, light beams are coherent if they combine like waves (by adding the amplitudes of the beams) while they are incoherent if they combine like particles (by add1

obscuratio, facta per solam additionem luminis. In fact, Grimaldi did not really observe interference (Kipnis,1991, p. 135), but his happy turn of phrase was remembered.

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ing the intensities, i.e. the square of the amplitudes, of the beams). As a consequence of these subtle effects, which light displays dependent on the experimental setup, statements about coherence demand and in fact are supported by solid proofs presented in the scientific literature. In contrast, many of the papers from the period 1980–2005 on coherence of ultra-weak photon emission contain speculative statements inspired (or not) by experimental results. Hence, the important purpose of this chapter is to assess, in the context of currently accepted viewpoints in physics, the solidity of the conclusions that the authors have drawn from their data. Some basic terminological relations should be explained in the beginning. The terminology used in quantum mechanics and quantum field theory often occurs in UPE literature, where the term coherence may refer either to (i) wave functions solving the Schrödinger equation or (ii) light, which is, strictly speaking, not the same (although related) and often creates confusion. In quantum mechanics, coherence is an intrinsic property of wave functions and once decoherence occurs (i.e. loss of wave function coherence – collapse of wave function), the system often behaves classically. Therefore, quantum behavior is equated to coherence by some authors, but it is reasonable only when speaking about wave functions. In this chapter, which deals with the coherence of light, one cannot directly equate either non-classical (quantum) with coherent light, or classical with incoherent light. Generally, the quantum optical framework can explain all states of light. The classical framework can explain only some of them and those can be called classical. The states which can be only explained in a quantum framework are usually called purely quantum states. The coherence of light can be both of classical and quantum character, thermal states of light (see below) can be described in classical and quantum framework, while certain states can be described only in a quantum framework (e.g. some squeezed states).

2.1. Classical vs. quantum coherence of light The coherence in classical physics typically describes how the intensities of two waves combine. If the intensity of the combined wave is the intensity of the sum of the amplitudes of the two waves, then we say that the waves are fully coherent and interference effects are maximal2. If the intensity of the combined wave is the sum of the intensities of the two waves, then we say that the waves are fully incoherent and do not interfere. Partial coherence describes intermediate situations between incoherent and fully coherent waves. Note that there are several types of coherence, which influence the visibility of interference fringes: temporal (refers to the correlation of the field between two times), spatial (refers to the correlation between two space points), spectral (refers to the correlation of field frequencies between

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two points) and polarization (refers to the correlation of the polarization of light fields). A quantum field theory of optical coherence is given by Mandel and Wolf (see Chapter 12 in Mandel and Wolf, 1995). Mathematically, instead of describing the light field by functions as in the classical case, a quantum treatment describes the light field by operators and quantum states. Field operators destroy (annihilation operator) or create (creation operator) a “particle” of the field – a photon. A major consequence from quantum optics is that light can behave in ways that are inexplicable by classical physics, e.g. noise reduction in squeezed states, or entanglement of photons over a large distance.

3. Models for UPE photons and their photocount distributions The coherence properties of UPE were investigated experimentally mainly by measuring the distribution of counts produced by UPE photons with a photomultiplier2. A few studies were also performed using a CCD camera (e.g., Kobayashi et al., 1999). Before introducing the basic models of states of light for photocount distributions we want to bring awareness of two important aspects. First, photocount distributions show the probability for a number of counts to be detected in finite time interval t (see e.g. Fig. 1). Such a distribution is one of the tools to describe statistical and coherence properties of light. The motivation behind this method is to relate the photocount distribution to the state of the biological system. Even though photocount distributions are rather easy to measure and give a hint on the possible states of light and do not require any sophisticated measurement system, they cannot unambiguously determine whether UPE is coherent or not. The reason is that specific photocount distributions cannot be uniquely attributed to specific states of light, i.e. similar or identical photocount distributions may come from different states of light. Yet, a solid method that quantifies coherence time or length of radiation comes from interferometric measurements. But while they are standard in quantum optics, they were not so far applied to UPE from biological systems, mainly because of experimental difficulties, which arise from the small number of photons and the nonstationarity of biological systems. Besides, the quantum nature of UPE was already suggested by Walter Stempell in 1932 (long before quantum electro-

Photon detectors do not measure the amplitude of light (i.e. its electric field E(r, t)) but its intensity, which is the average over time of the square of the amplitude of light.

2


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dynamics was fully established)3. Indeed, since chemiluminescence is ultimately interpreted as a quantum phenomenon (photons are quantum objects), the source of UPE is quantum mechanical in nature. However, when a photocount statistics is considered to be classical, it is not because its source is classical but because it can be described by a (positive) probability distribution. In the literature on UPE, the most commonly encountered types of states of light are the so-called coherent, squeezed, and thermal states, which we explain in the following section. Coherent states were discovered by Schrödinger (1926), rediscovered by Schwinger (1953), and further studied by Glauber (1963) who called them coherent states. Coherent states are now a standard tool of quantum optics (Mandel and Wolf, 1995). From the conceptual point of view, coherent states are those quantum states that correspond to classical electromagnetic waves. For instance, a classical varying current (a simple source of electromagnetic waves – a piece of electric wire carrying a macroscopic varying current, I(t), for instance) gives rise to a coherent state of the photon field (Itzykson and Zuber, 1980). The photocount statistics of a system in a coherent state gives rise to a Poisson distribution (see Fig. 1)4. A Poisson distribution is a sign of classical light field. Its variance is equal to its mean: ⟨△n2⟩ = ⟨n⟩ (see also Fig. 1). Departure from a Poisson distribution can be measured by the so-called Fano factor F such that F = ⟨△n2⟩/⟨n⟩ or by the Mandel parameter Q = F − 1. A photocount statistics is superPoissonian if F > 1 and Q > 0, it is sub-Poissonian if F < 1 and Q < 0. A super-Poissonian distribution can be classical but a sub-Poissonian distribution is a purely quantum state of light: it cannot be described by a positive probability distribution.

3 According to Stempell, “… die Quantennatur der Strahlung dabei in die Erscheinung tritt.” (Stempell, 1932, p. 63). 4 Keep in mind that also other states of light can yield Poisson-like distribution of photocount statistics.

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Figure 1. Poisson distributions for four different average values of photon counts ⟨n⟩. A reading example: At an average signal intensity of ⟨n⟩ = 3 counts per time interval t, the probability to detect 5 counts in time interval t is 0.1 (i.e. 10%).

However, one has to be careful to avoid experimental and instrumental artifacts, which can also lead to observations different from Poisson distribution (e.g. super- or sub-Poisson) of photocounts even when measuring classical and thermal light that would normally lead to a Poissonian distribution if it was measured correctly. Super-Poisson distribution can be caused by nonstationarity of the light source such as (i) a modulation intensity of the photon signal by periodic internal or external factors or (ii) bursts of photon emission caused by stochastic processes. However, superPoisson distribution caused by such nonstationarities has nothing to do with squeezed states of light. In squeezed states, the dispersion (uncertainty) of one variable is reduced at the cost of an increase in the dispersion of the other canonical variable (amplitude vs. phase, or position vs. momentum). Various squeezed states were used in the UPE literature, but the most general ones are called two-photon coherent states (generalization of states which have minimum uncertainty) (Yuen, 1976). They have become standard states of quantum optics (Mandel and Wolf, 1995, p. 1046) and their photocount statistics is known (Mandel and Wolf, 1995, p. 1050). Squeezed states are interesting because they can manifest lower intrinsic noise (fluctuations around mean) than coherent light, a feature which classical light cannot achieve (see also


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Fig. 2). The lower the intrinsic noise (related to uncertainty), the higher the efficiency of such states to transmit information. A thermal state of light can be obtained by filtering a spectral band from a blackbody (thermal) radiation. Thermal states are classical, i.e. they can be described within the framework of classical physics, and represent a model of random light with very low coherence. Usually a thermal light is not emitted by a single oscillator but by a large number of oscillators (also called modes or degrees of freedom). The number of degrees of freedom M can be estimated as the product of a time degeneracy (Mt) and a space degeneracy (Ms), where the time degeneracy is the ratio of the measurement interval t over the coherence time (Loudon, 2000, p. 97) and the space degeneracy is the number of incoherent oscillators in the source (Mosset, 2004). The photocount statistics of a thermal source depends on the number of degrees of freedom M (Mandel and Wolf, 1995, p. 680 and 731) (see also Fig. 3). Since the question whether photons are in a coherent or a thermal state is recurrent in the UPE literature, it is important to know how to distinguish between them. However, since photocount statistics of thermal light becomes equal to that of a coherent state when M is large, photocount statistics is not able to discriminate between a coherent and a thermal state with many modes. This can in particular be seen by the relation between variance and mean in a thermal state: ⟨△n2⟩ = ⟨n⟩ + ⟨n⟩/M . We see that, when M is very large, we recover ⟨△n2⟩ = ⟨n⟩ as for a coherent state (Fig. 3). We know that the number of modes M is generally very large for chaotic sources (Jiang et al., 2003), bringing the relation between variance and mean of photocount distribution close to that of a coherent state. Therefore, since the average number of photons ⟨n⟩ is particularly small in UPE experiments, we expect that it will be difficult to distinguish thermal UPE from coherent UPE based on photocount distributions.

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Figure 2. Squeezed state photocount distributions. The upper graph shows a socalled amplitude squeezed light, where fluctuations of the number of photon counts (i.e. the amplitude) are reduced leading to a narrower photon distribution. The middle graph shows a Poisson distribution of photons compatible with a coherent light. The lower graph describes phase-squeezed light with reduced phase fluctuations and increased amplitude fluctuations leading to a broader distribution. Dots in all three graphs are measured, i.e. observed values from technical tunable squeezed light generating sources, the bars are theoretically calculated, i.e. expected values. The graphs are from Breitenbach (1998).


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Figure 3. The thermal field photocount distribution (blue line) approaches the Poisson distribution (red line) for a large number of modes M, i.e. for a large number of independently radiating sources (molecules, atoms) or from a single source with very short coherence time compared to time interval of measurement. The average intensity of the photon signal ⟨n⟩ is the same for all displayed distributions.

4. Experimental measurements of the photocount statistics of UPE There are several tens of experimental works which aimed to study photocount statistics of biological UPE related sources from different species from bacteria to man. List of them can be found in Table 1. We assess the works which characterize the state of art most accurately in this section.

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Sample from

References

Chemicals Luminol

C8 H7 N3 O2

Shen et al., 1993

Polystyrol

(C8 H8)n

Popp, 1992a

Symbiotic bacteria

Photobacterium phosphoreum

Kobayashi et al., 1998

Nitrogen-fixating symbiont

Bradyrhizobium japonicum

Shen et al., 1993

Umbrella or cap algae

Acetabularia acetabulum

Popp, 1992a

Dinoflagellate

Prorocentrum elegans

Popp, 1992a

Dinoflagellate

Gonyaulax polyedra

Popp, 1992a; Gu, 1995; Chang, 2008a

Slime mold (also multicellular)

Dictyostelium discoideum

Kobayashi and Inaba, 2000

Lichen

Parmelia physodes

Schirmacher, 2008

Lichen

Parmelia tinctorum

Bajpai, 2004, 2005a

Lichen

Parmelinella wallichiana

Bajpai, 2005b, 2007

Lichen

Xanthoria parietina

Bajpai, 2008

Prokaryotes

Eukaryotes, unicellular

Algea-mushroom symbiont

Plants Silver fir

Abies alba

Schirmacher, 2008

Arabica coffee

Coffea arabica

Gallep et al., 2004

Robusta coffee

Coffea canephora

Gallep et al., 2004

cucumber seedlings

Cucumis sativus

Popp et al., 1981; Shen et al., 1993

Cucumber

Cucumis sativus

Gu, 1995

Elder bush leaflet

Sambucus sp.

Popp and Shen, 1998

Banyan tree

Ficus microcarpa

Schirmacher, 2008

gum tree (rubber plant)

Ficus elastica

Gu, 1998

mungbean seedlings

Phaseolus aureus

Shen et al., 1993; Popp et al., 1994; Popp and Shen, 1998

Table 1. List of experimental works studying photocount statistics of biological UPE related sources from different species from bacteria to human.


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Purple leaf plum

Prunus cerasifera ‘Nigra’

Schirmacher, 2008

Oak

Quercus robur

Schirmacher, 2008

soybean seedlings

Glycine max

Popp, 1992a; Popp et al., 1994

Soybeans

Glycine max

Chang and Popp, 1998

Stinging nettle

Urtica dioica

Schirmacher, 2008

Waterfleas (Crustacean)

Daphnia sp.

Popp, 1992b; Gu, 1995; Gallep et al., 2007

Fireflies (Insects)

Lampyridae

Chang, 2008a

Thailand firefly (Insects)

Lampyridae

Popp, 1992a

Chicken embryo, brain

Gallus gallus domesticus

Chang, 2008a

Body

Homo sapiens sapiens

van Wijk et al., 2006b

Body of meditating subjects


van Wijk et al., 2008

Hand of a multiple sclerosis patient


Bajpai and Drexel, 2008

Hands


van Wijk et al., 2010

Animals

Human

Table 1. Continued

4.1. Non-biological sources Photocount statistics measurement of weak luminescent sources was performed for solid-state ZnS:Cu luminophores (Konak et al., 1982), luminescent glass (Konak et al., 1982), and single molecules in microdroplets (Hill et al., 1998). All these experiments were analyzed in terms of thermal or Poisson statistics. The photocount statistics of diodes was found to be either Poissonian (Kobayashi et al., 1998) (for LED) or super-Poissonian (Huang et al., 2005) (for avalanche photodiodes). The chemiluminescence of a standard chemical reaction shows Poisson statistics (Collinson and Wightman, 1995). These findings prove that also random light (there is no reason to expect coherent light from e.g. chemical reactions or glass luminescence) can manifest photocount distribution close to Poissonian, as predicted by the theory of thermal states. For the following discussion, it is important to stress again that Poisson statistics is not a proof of the existence of a coherent state of light. For example the superposition of a large number of independent equilibrium renewal

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processes, each with a small intensity, behaves asymptotically like a Poisson process (Lindner, 2006).

4.2. Biological sources There are not many works on the photocount statistics of UPE that are at the level of the quantum optics literature, without over-interpretation of the results. As an example, Kobayashi et al. (1998) is a careful and useful investigation of the photocount statistics of a time-dependent system. The authors measured the photoluminescent bacterium Photobacterium phosphoreum and observed a Fano factor significantly greater than one, which indicates a superPoissonian statistics. They did not interpret this finding as an indication of a squeezed state of light but analyzed it in terms of a chaotic source (using eq. (41) of Saleh et al., 1983) with “clustering of excitation and emission”. Another paper from the same authors represents a still more thorough investigation of the measurement of UPE photocount statistics; the experimental setup as well as possible artifacts is described in great detail (Kobayashi and Inaba, 2000). They discuss the measurement of the Fano factor in the presence of dark current and for a time-dependent source. They measured the photon statistics of Dictyostelium discoideum and observed the variation of the Fano factor during the early stage of development and after starvation. They found super-Poisson statistics (i.e. photocount distribution with a width greater than a Poissonian distribution), which they interpreted, as in their previous work, to be caused by clustering of excitation and emission processes where the optical field is composed of a sequence of independent flashes initiated by Poisson random time events. No relation to squeezed states, which can also manifest super-Poisson statistics, was mentioned. This article represents a quality benchmark for all UPE photocount measurements in terms of careful verification of the experimental setup and rigorous interpretation of the data. Kobayashi and coll. also discuss the measurement of photocount statistics with CCD cameras (Kobayashi et al., 1999; Kobayashi, 2003, 2005). The third remarkable publication on this subject is the thesis by Schirmacher (2008) who focused on the detection of possible squeezed states of UPE. Half of the thesis is dedicated to (i) a theoretical analysis of the quantization of the electromagnetic field and (ii) the theory of photodetection. He also performed theoretical simulations of the influence of the number of modes and of the detection efficiency on the photocount distribution of squeezed states. He measured photon statistics in Parmelia physodes, Prunus cerasifera ‘Nigra’, Abies alba, Ficus microcarpa, Urtica dioica, Quercus robur and compared them with the light beam of a He-Ne laser. He observed only super-Poissonian statistics and did not find conclusive evidence of a quantum behavior of light.


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There are several other scientists contributing to the field of UPE statistics and carefully performing and analyzing their experiments. For instance, Shen et al. measured the photocount statistics of cucumber seedlings, mungbean seedlings, rhizobium bacteroids, autooxidized luminol, laser light and randomized laser light (Shen et al., 1993). They found that the Mandel parameter of the photocount statistics of He-Ne laser, cucumber seedlings, mungbean seedlings, rhizobium bacteriods is close to 0 (compatible with a Poisson distribution). Yet, signals from photomultiplier detector noise, randomized laser beam or auto-oxidation of luminol showed a Mandel parameter higher than 0 (indicating a super-Poisson distribution). The authors openly stated that their aim was mainly to provide experimental data and discussed the issue of distinguishing the properties of light solely from photocount distributions. There are also other authors, who measured photocount statistics and fitted their results to statistical distributions coming from squeezed states of light (without assuming that the light field they measured was actually in a squeezed state). The parameters of these distributions enabled them to distinguish various samples (i.e. obtain “fingerprints”). For instance, van Wijk and coll. found specific UPE parameters for various parts of a human body (van Wijk et al., 2006b, 2010). Other authors indulged themselves into more speculative interpretations. Fritz-Albert Popp pioneered the experimental work on the statistical properties of biological ultra-weak photon emission and motivated many scientists to work on this topic. However, many of his interpretations of experimental results are not consistent with the standard physical framework and, hence, are not generally accepted. Popp introduced the working hypothesis that the biological UPE originates from a biological coherent photon field that, further, is regulating biological processes (Popp and Ruth, 1977). This hypothesis was inspired by two indications. On the one hand, Popp had investigated several polycyclic hydrocarbons in order to find a correlation between their electronic properties and carcinogenic activity (Popp, 1976). He proposed that the mechanism of the action of the cancerogenic substances was the disturbance of the excitation cellular photon field at a certain energy level that is related to DNA repair (Popp 1976; Li, 1992a, p. 117). On the other hand, the general idea of coherent electrically polar vibration states in GHz–THz region in metabolically active cells, which had been postulated by Fröhlich (1968), was embraced by Popp as a theory generally supporting coherent processes in biology. He further assumed, with reference to the model of Li (1992b), that the DNA in the cells behaves as a low level excimer laser generating a coherent photon field. From this time on, the experimental data obtained in Popp’s group have been attempted to fit the coherence theory of biological ultra-weak photon emission. In the following, we will highlight four specific points in the research work of Popp that are controversial either because they strongly de-

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viate from standard biophysical concepts or because they lack generally accepted experimental evidence: • “DNA represents active photon stores which are governed by Bose condensation” (Popp et al., 1981, p. 312; Popp, 1981, 1983; Popp et al., 1984; Popp, 1986b,a, 1995). Whereas DNA is known to be an auto-fluorescent substance, direct storage of photons on longer time scales (minutes, days) is not substantiated. Rattemeyer et al. (1981) performed experiments where DNA manifested different delayed luminescent intensities with different concentrations of ethidium bromide intercalator causing the unwinding of DNA and used these results as a proof of DNA photon storage. However, ethidium bromide is itself a fluorescent substance (Zhang et al., 2012) so it is hard to draw any conclusions from this experiment. Besides, no other group ever reproduced that result. • “The Poisson photocount distribution of photons detected from biological systems is a signature of a coherent field.” Popp was well aware that Poisson photocount distribution can also come from a thermal field with many modes, but was claiming that an extremely strong mode-coupling is taking place in biological systems that reduces the effective number of modes M to approximately 1 (Popp, 1986b). While the general idea of mode coupling in physics and biology is not unreasonable (Swain 2006, 2008), such an extreme coupling of modes of an optical field in biological conditions is not experimentally confirmed and appears to be far-fetched. • “Hyperbolic decay of delayed luminescence is a sufficient condition for coherence” (Li et al., 1983; Popp et al., 1984; Popp and Li, 1992; Li, 1992a; Popp and Li, 1993; Popp and Yan, 2002; Yan et al., 2005). As such, the hyperbolic decay of delayed luminescence is not generally considered to be a proof or a sufficient condition for coherence in quantum optics community. Furthermore, in some of the papers, several conceptual and mathematical mistakes were identified (see Salari and Brouder2011, for a detailed investigation of one of these papers.) In addition, the state of light met in delayed luminescence is different from the state of light of autoluminescence (UPE) because the former is time-dependent while the latter is not. Therefore, conclusions from the study of physical parameters from measurements on delayed luminescence cannot be directly used to determine parameters of UPE. • “Photon emission from biological systems comes from a fully coherent electromagnetic field which serves as a basis for communication in living tissues.” (Popp et al., 1988, p. 577). Again and as previously explained, this general claim is not substantiated by any available data.


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Reviews of Popp’s work can be found in some of his own papers (Popp, 2003a,b, 2005, 2009)5. He and his co-workers developed fine experimental setups, imagined clever experiments with very interesting results, but their interpretations were too far-fetched. On the one hand, this attitude took Popp away from the scientific community and brought the subject of “biophotons” into disrepute. On the other hand, F.-A. Popp’s work was important in the sense that he formulated his visionary hypotheses and designed first experiments to test the field concept in biology pursing them further to such an extent that this scientific field attracted attention of many researchers as well as the public. The articles published by R. Bajpai focus on squeezed states instead of coherent states in order to analyze UPE photocount statistics. His main working hypothesis is that the photon field in biological systems is in a squeezed state (Bajpai et al., 1998). Squeezed states were used for modeling hyperbolic decay of delayed luminescence and photocount statistics of spontaneous UPE from lichen Parmelia tinctorum (Bajpai, 2004, 2005a, 2007). The squeezed state distributions provide a flexible way of analyzing UPE photocount statistics because they are mathematically described by 4 parameters with which one can fit various shapes of experimental distributions. As such, it is an interesting model. However, as for the Poisson distribution, the fact that this model fits experimental data does not necessarily mean that UPE is in a squeezed state. It is generally impossible to deduce the state of light from a photocount distribution. Higher order correlation functions of light must be measured as well, using e.g. Hanbury-BrownTwiss-like interferometer, in order to provide evidence for squeezed or other non-classical states of light. Yet, as such, UPE statistics is an ongoing research branch and can definitely bring interesting results, when carefully elaborated. With a background in quantum optics, Gu describes (1995) the source of UPE as a three-energy level system. He introduces super-radiance and a model involving the sum of two coherent states (Gu, 1995). Gu, furthermore, discusses non-classical light and wonders whether there are … nonclassical effects in biological systems. He, furthermore, considers the biophoton field as having the property … to ensure an extremely high efficiency of informational transfer in life activity (Gu, 1998). These are stimulating statements but again just speculations. Chang (2008a,b) describes several coincidence-counting experiments (i.e. experiments with two detectors setups which aim to detect simultaneous photon emission from a single source). Photocount distributions were measured for Dinoflagellates, chicken embryos and fireflies Lampyridae. 5

Fritz-Albert Popp is not active in research anymore and his laboratory, International Institute of Biophysics, near Neuss, Germany, has been closed.

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While the results as such are interesting and suggest all types of Poissondistributions (namely normal, super- and even sub-Poisson photon statistics), her interpretations also contain speculative and unsubstantiated statements.

5. Fractal and chaotic properties of UPE time series Independently from its physical photonic nature, UPE can also be viewed as a time series signal. Therefore signal analysis methods for time series that are used in other research fields, can be applied to UPE signals as well. The signal parameters of UPE originate from the physical properties of light and the biochemical dynamics, both being intrinsically connected at the molecular and atomic level. From a biochemical point of view, biological processes, which generate UPE are chemical reactions involving reactive oxygen species and free radicals (see Chapter 6 of this book). Chemical and biochemical reactions can exhibit time and space periodic oscillations (Epstein and Showalter, 1996; Epstein et al., 1983; Savi, 2005; Lloyd, 2005, 2009), they can be pulsating as well as displaying complex chaotic and/or fractal dynamics (Kopelman et al., 1988; Aon et al., 2000; Benichou et al., 2010; Kopelman, 2010). Thus, it is natural to expect that also biological UPE could exhibit oscillatory and chaotic fractal behavior. Indeed, it has been demonstrated that chemiluminescence due to a Maillardaminocarbonyl-reaction in aqueous solutions undergoes periodic and aperiodic oscillations in time (Voeikov et al., 2001 a,b).

Figure 4. UPE time series signals are taken from a 7 cm2 surface of germinating mung beans. The signal has an appearance of a random signal. The average dark count of the detector was 13 counts/s (detector H7360-01 PMT module).

Beloussov has shown that the photon emission signals from developing loach embryos exhibit specific frequency and autocorrelation patterns (Bel-


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oussov et al., 2003, 2002a,b). Such clear periodic dynamics in UPE are extremely interesting. Contrary to the usual meaning of the word, chaos is in mathematical terms a … Complex output that mimics random behaviour that is generated by a simple, deterministic system (Liebovitch, 1998, p. 124). Chaotic and fractal dynamics are found in chemical and biochemical systems but manifest themselves also on the level of organisms (Stanley et al., 1999; Ivanov et al., 1999). Obviously, biological systems exhibit higher structural complexity than simple homogenous chemical systems. Biological structural complexity described in terms of fractals can be found in the literature from the level of proteins (Tejera et al., 2009) and organelles (Keough et al., 2011) to the level of physiological systems (Mainster, 1990; Liebovitch, 1998). As it is very natural to assume that the processes occurring within fractal geometrical landscapes will also manifest fractal dynamics6, it became a straightforward concept for several authors to apply the principles of fractal theory to biological UPE signals. The Fano factor F(T) (where we explicitly denote the duration T of the measurement window) is one of the simple measures to assess whether the signal manifests fractality (Teich, 1989). For a random Poisson process in which fluctuations in photon counts are uncorrelated, F(T) is approximately 1 for all window sizes (Teich, 1989, 1992). For a fractal process, F(T) increases as a power of the window size and may reach values greater than 1 (Teich, 1989, 1992). The slope of the doubly logarithmic plot of F(T) is the scaling exponent, often denoted as α, which is the power to which fluctuations in photon counts on one time scale relate to those on longer time scales. The scaling exponent is useful for assessing self-similarity, one of the features of fractal signals. The Fano factor as well as the first four statistical moments (mean, variance, skewness, kurtosis) of the photocount distribution have been used by the group of Van Wijk to characterize the UPE signal from three body locations of a single human subject (van Wijk et al., 2006a); they found that the Fano factor as well as statistical moments were different for each body location. Studies in this direction were further developed because authors considered the Fano factor to be a useful parameter to fingerprint the UPE signal. UPE signals from the dorsal and palmar side of both hands of 50 human subjects (van Wijk et al., 2010) and from two pre- and post-meditating human subjects (Van Wijk et al., 2005) have been measured and analyzed for F(T), statistical moments and doubly logarithmic plot of F(T). Fifty human subjects showed F(T) = 1 for T < 3 s and rather large variation of their Good example of relation of structural and dynamical fractality can be seen in the heart (West and Deering, 1994), where the His-Purkyne system which innervates the myocard has geometrically fractal branching. Some authors (West and Deering, 1994) stipulated that the fractal branching of the electrical depolarization wave results in fractal scaling in dynamics of heart rate variability.

6

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F(T) for T greater than a few seconds. A difference in F(T) of UPE from preand post-meditating human subjects has been observed. Furthermore, a doubly logarithmic plot of F(T) dependence has also been used (van Wijk et al., 2011) to characterize UPE signals of respiratory bursting neutrophils. The authors suggested that Fano factor analysis could provide information regarding leukocyte interactions because any deviation from Poisson statistics contains information within the sequence of photon counts events about the cell population as a collective phenomenon. However, using Fano factor without signal preprocessing and careful observation of the possible technogenic origin of UPE signal fluctuation can be tricky. Fano factor of the background (detector) noise, which can be generally different from that of the UPE signals, needs to be taken into account especially for those cases where the signal to noise ratio of UPE is low. For example, one can observe different F(T) of two statistically identical UPE signals simply because one has a lower intensity and is closer to the noise level of the detector. The Fano-factor itself is only a good measure for signals without any decreasing or increasing trend, for otherwise it will yield misleading results. Since shuffling (randomization) of data with decreasing or increasing trend removes both the trend and long-range correlations, it cannot be generally used as a surrogate signal. There exist a number of other and much more developed methods to free a signal from trends and obtain a more reliable quantification of fractal and chaotic parameters of the signal (Stanley et al., 1999). Scholkmann et al. (2011) pioneered the use of more advanced fractal analysis methods on UPE signals: Multifractal detrended moving average analysis of UPE signals from germinating wheat seedlings was used to differentiate between two grades of intoxication with potassium dichromate. It is essential to extend the advanced signal analysis of UPE signals to see if the obtained parameters can have a differentiating and diagnostic “fingerprint” character.

6. Conclusion The conclusion of our review is that, up to now, no reliable estimate of the coherence of UPE was made by the different methods of photocount statistics measurement. The presence of coherence seems to follow from a straightforward reasoning: a living organism must be in some coherent state because it is obviously not in thermal equilibrium (del Giudice et al., 2005). However, the actual situation is more subtle: on the one hand, a thermal source can emit partially coherent light, even close to the source (Greffet et al., 2002), and, furthermore, independent thermal sources can


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produce two-photon interference (Zhai et al., 2006)7 while, on the other hand, the organization required to maintain life has no a priori reason to imply that UPE is in a coherent state. Moreover, thermal states and coherent states are two extremes of a very broad range of possible states of light. What we would need is to actually measure the coherence length and time of UPE. The UPE coherence times given by Popp (10 days8) and Bajpai (5 hours9) seem to be completely off the mark. Standard methods in quantum optics can deliver more reliable information on coherence and statistical properties of UPE of living systems. Coherence parameters could be quantified by measuring light interferences or light correlation functions. A non-classical, i.e. quantum nature could be assessed by using a Hanbury-Brown-Twiss interferometer, but the extremely low intensity of UPE makes these experiments highly challenging. However, for the concept of coherence in biology as such, it needs to be noted that the coherence of vibrational and spin dynamics in biomolecules is gaining acceptance among biophysicists (Cimei et al., 2002; Gruia et al., 2008; Liebl et al., 1999; Engel et al., 2007; Parson, 2007; Wolynes, 2009). Furthermore, there are indications that other signal properties of biological UPE than those studied in quantum optics, such as coherence, are naturally oscillatory, complex (chaotic) and fractal. Thus, suitable methods adapted from statistical physics and already used for other biological signals to uncover “hidden information” (Goldberger et al., 2002) may be also used to analyze the UPE signals.

7. Acknowledgements Ch.B. thanks Reinhard Honegger for his kind explanations of the relation between coherent states and factorizing correlations. Historically, M.C. deeply acknowledges F.-A. Popp for introducing him to the field of biological ultra-weak photon emission and to R. P. Bajpai for extensive discussions and inspirations. M.C. is supported by the Czech Science Foundation, GA CR, grant no. GP13-29294S.

7 One should keep in mind that two-photon interference is not the interference of two photons (Pittman et al., 1996). 8 “A reasonable coherence time is the lifetime of cell organelles (for instance, mitotic figures) of about ten days” (Popp, 2009, p. 59). 9 “The signal was, therefore, coherent for 5 hr” (Bajpai, 1999).

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Chapter 10

Cellular electrodynamics in kHz–THz region Michal Cifra Institute of Photonics and Electronics, The Czech Academy of Sciences, Chaberska 57, 18200 Prague 8, Czech Republic Abstract: We review here theories and evidence of a cellular electrodynamic field in the kHz–THz region and its biological relevance. The endogenous cellular electrodynamic field has been predicted to contribute to the organization within the cell and to interactions among the cells. Any cellular pulsed or oscillatory process, which involves electrically charged or electrically polar molecular structure, generates an electrodynamic field. Energy supply to and low damping of an oscillatory process are necessary conditions for generation of a field, which is of higher intensity than the field of thermal origin. We describe cellular processes, which can give rise to an electrodynamic field in the kHz– THz spectral region and are likely to be fulfilling necessary conditions of energy supply and low damping. Our focus is on microtubule electromechanical vibrations, but also electronic conduction processes in DNA and proteins in general are briefly reviewed. We also review and assess experimental works aiming to detect cellular radiofrequency fields directly or indirectly. We conclude that evidence for the necessary physical conditions for cellular electrodynamic field is accumulating. However, there is still little direct experimental evidence for kHz–THz electrodynamic field of nonexcitable cells. We believe that near future can bring significant progress in this research field if appropriate cutting edge technologies in detection techniques are used. Correspondence/Reprint request: Dr. Michal Cifra, Institute of Photonics and Electronics, The Czech Academy of Sciences, Chaberska 57, 18200 Prague 8, Czech Republic. E-mail: [email protected]

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1. Introduction Biological phenomena that cannot be reduced to direct chemical “contact” interaction between molecular partners have always either attracted attention of some scientists or scared off and discouraged others due to an apparent taste of mystery as was the case, e.g., in the field of bioelectricity and electrophysiology (Geddes and Hoff, 1971; Cajavilca et al., 2009). However, a rigorous scientific description of bioelectric phenomena became possible with the conceptual and technological progress enabling the clarification of physical processes underlying electrophysiology. Nowadays, the existence of electric activity of cells is well accepted; its biological importance in case of electro-excitable cells is indisputable, e.g., for nerve and muscle cells of higher organisms. In addition, these electrophysiological phenomena are observed and studied for frequencies of a few kHz (Buzsaki et al., 1992; Collins et al., 2001) and are not expected to exist at higher frequencies (> 1–10 kHz). Yet, let us imagine, on the one hand, a physicist who knows that the electromagnetic field on Earth (also due to cosmic radiation) displays much broader frequency spectra (see, e.g., in Chapter 2 of this book). He may ask whether biological systems that evolved on Earth generate an electromagnetic field of a broader frequency range, say in kHz–THz region, than the one which is in intense focus of current electrophysiology? A biologist, on the other hand, might want to know whether such frequencies – assuming they exist – have a function, i.e. are of biological relevance. Could such cellular electromagnetic fields explain biological phenomena, which we either had overlooked or neglected because we considered them as artifacts since they did not fit into our concepts? The latter being, for example, interactions between bio-molecules that are faster and occur over larger distances than allowed by the classical model of diffusion-based distribution of molecules. Furthermore, if there was evidence - from at least a small number of experiments – for a high frequency biological electromagnetic field, we would like to know about the structures and processes that generate these cellular electromagnetic fields. Electromagnetic fields are physical quantities directly measurable via their force effects. Therefore, using the proper technology, experimental evidence for cellular electrodynamic fields can be obtained1. This chapter 1 There is no strict difference between the terms electromagnetic and electrodynamic fields. With the prefix bio-, a term bioelectromagnetism is used to denote endogenous electromagnetic fields of biological systems. Yet, again, literature on bioelectromagnetism almost exclusively deals with low frequency fields (< kHz), while we stress the high frequency fields (> kHz). One can find in literature both terms bioelectrodynam-

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summarizes the foundation for cellular kHz–THz electrodynamics. It thereby focuses also on the cellular origin of the field.

2. Why to research cellular electrodynamic fields The working hypothesis of several authors is that the endogenous cellular electrodynamic field has an organizing function within cells and mediates interactions between cells.

2.1. Role of fields in intracellular processes The role of the endogenous cellular electrodynamic field has been predicted as (i) transporting reaction components and (ii) driving the kinetics of chemical reactions (Pokorný et al., 2005b,a; Pokorný, 2001). The theory on the cellular field, furthermore, predicts that certain cellular structures create spatially and dynamically complex patterns (local minima and maxima of field intensity) of the electrodynamic field (Cifra et al., 2010; Havelka et al., 2011; Cifra et al., 2011b). This inhomogenous electric field pattern acts by force on molecules adding a deterministic component to their diffusion movement and thereby helping to organize the movement of the reaction components (Pokorný et al., 2005b,a; Pokorný, 2001). In addition, the spatial and temporal organization of larger structures of the cell, i.e. the positioning of organelles and macromolecules) can be influenced by the electrodynamic field in ways similar to those described above (Cifra, 2012). Note that the recruiting of molecular reaction partners by long-range electrodynamic interactions has already been predicted by Fröhlich (Fröhlich, 1968b, 1972, 1970), later on again by van Zandt (Van Zandt, 1978) and recently re-assessed (Preto et al., 2012). Furthermore, electrodynamic processes are assumed to play a significant role in cellular signaling (Priel et al., 2005, 2006) as well as energy transfer (Cope, 1973). Finally, some researchers suggest that the disturbance of the endogenous cellular electrodynamic processes plays an important role in cancer (Pokorný, 2012).

2.2. Role of fields in intercellular processes Multiple authors have performed experiments that show effects at distances that were not predicted by a molecule-based diffusion model. One class of experiments relates to the so-called dielectrophoretic effect of cells on surrounding particles. This was extensively investigated by Pohl et al. (Pohl, 1980b,a, 1981; Roy et al., 1981; Pohl et al., 1981;

ics or cellular electrodynamics, the former being more general without scale limitations and the latter limited to the scale of the cells.

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Pohl, 1982, 1983; Rivera et al., 1985). In this effect, cells are attracting or repulsing micron sized dielectric particles. To test these assumptions, Pohl et al. were changing (i) the conductivity of the medium, (ii) the dielectric constant of the particles, or they were (iii) switching off the metabolism of the cells. They concluded that the observed changes in movement of the particles around individual tester cells were caused by an oscillating electric field that is, furthermore, generated in accordance to the metabolic activity of the cells. Another class of theories and experiments was focused on electromagnetic force interaction between cells. Based on the assumption of Fröhlich’s coherent electric oscillations generated by cells, Pokorný theoretically analyzed, the mutual attraction of cells (Pokorný, 1980; Pokorný et al., 1983; Pokorný and Wu, 1998). His results suggested that cells should be able to interact electromagnetically (attract or repulse) up to the distance of 10 micrometers. There were also several experimental tests carried out on leukocyte sedimentation rate and adherence (Jandová et al., 1987). Sedimentation rate of cells and measured force between the cells and glass slides substrate coincided with theoretical predictions of adherent force based on cellular electrodynamic activity. Most famous are the results of Rowlands et al. who observed that roleaux formation of erythrocytes does not simply follow Brownian laws of motion. It was suggested that the cellular electrodynamic fields generated as described by the theory of Fröhlich gives a plausible explanation for this complex group of cellular interactions (Rowlands, 1983; Rowlands et al., 1981, 1982; Sewchand and Rowlands, 1983). Fröhlich predicted in his theory that coherent electric oscillations of biosystems mediate mutual long-range (on cellular/ molecular scale) resonance-like attraction. However, one has to be careful about experimental details and interpretation of the results. Many experiments on electrodynamic cellular interactions were performed with a focus on the optical field of cells of many species (see Table 2. in (Cifra et al., 2011a) or Ch. 8 in this book). There are, indeed, strong indications that the cells are able to interact through their endogenous photon emission under certain conditions. However, this refers to frequencies in the visible and UV region while here we focus on frequency ranges of microwaves and below. To summarize this section, there are many interesting theoretical predictions and experimental observations that take into account the cellular electrodynamic field. Moreover, some of the observations can be hardly explained without assumption of non-chemical interaction that acts over distance. Nevertheless, it needs to be emphasized again that one has to be very careful about experimental details and interpretation of the results as various other non-field-like physicochemical phenomena can contribute to the observed results.


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3. Which structures and processes generate the cellular electromagnetic field 3.1. Basis of electromagnetic field generation All objects, whether living or nonliving, are continuously generating electromagnetic fields due to the thermal agitation of the particles that possess charge. The thereby generated electromagnetic spectrum is described by Planck’s law for the ideal case of a blackbody in thermal equilibrium. Electromagnetic fields generated thermally have a random, non-coherent character. However, our question is whether the electromagnetic field of a biological entity is an electromagnetic field generated by an object due to its temperature or whether it is part of a biological property of a living system. Physically, living biological systems are thermodynamic systems in a nonequilibrium state (i.e., they have a different energy level than their surrounding) and they are open (i.e., they can transfer energy and matter through the system). Such systems may locally decrease entropy (increase order). Since living systems are not in a thermal equilibrium, their electromagnetic (or generally, vibrational) spectrum may also deviate from thermal spectrum given by Planck’s law. Furthermore, the important question is whether the generated biological electrodynamic fields can have a coherent component, since coherence enables very efficient energy and information transfer via the spatial and dynamic formation of interference patterns. The answer may be at least partially elucidated when we describe the structures and processes that are responsible for the generation of the cellular electrodynamic fields.

3.2. Basis for cellular electrodynamic field generation Various cell functions are associated with moving charges in cellular compartments and, hence, generate electrodynamic fields. For example, membrane depolarization or neuron firing at several hundred Hz (Buzsaki et al., 1992) generates oscillations of electric charges with higher harmonics creating an electric oscillations with a frequency up to 10 kHz (Collins et al., 2001). However, this phenomenon is limited to a group of specialized cells in higher organisms and not all cells in an organism are involved in the process of membrane depolarization. The question arises whether non-specialized cells that are not involved in cell membrane depolarization are also capable of generating electrodynamic fields, and if so how. A graphical summary of our working model for the generation of the cellular electrodynamic field is depicted in Figure 1. Generally we can distinguish three types of processes generating electrodynamic fields in cells: • Mechanical vibrations of electrically polar structures (proteins) (kHz–THz)

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• Free ionic oscillation (Hz–MHz) • Electronic oscillations (Hz–THz) Additionally, in combination these processes can form quasiparticles2. The above list of types of processes generating electrodynamic fields delivers the physically reasonable conceptual boundaries where to look for the realization of these processes in cells. As such, there may be multiple sources of cellular electrodynamic field finally combining into a spectrally and spatially complex total field. Yet, some general necessary conditions need to be fulfilled in order to generate nontrivial cellular electrodynamic fields: • Energy supply • Low damping of the oscillatory process: The term Quality factor (Q) is also often used in this context. Q is inversely proportional to the damping rate. If the damping is high, supplied energy quickly dissipates into all degrees of freedom, i.e. the system is heated up and the generated electrodynamic field is only thermal with very broadband frequency content.

3.2.1 Mechanical vibrations of electrically polar structures The most straightforward (mechanistic) approach explaining the generation of the cellular electrodynamic field is based on vibrations of electrically polar biomolecular cellular structures. Such vibrations and modes of biomolecules are broadly studied by multiple types of spectroscopies (Barth, 2007; Chou, 1988; Painter et al., 1982) and, hence are today widely acknowledged. It is not surprising that it was concluded that the frequency of vibrations depends on both the size and stiffness of the structure and the type of vibrational mode(s), since this is very well known from macroscopic physics. Probable structures that lead to the appearance of a cellular field are the intrinsic electrically polar structures such as most proteins (Wada and Nakamura, 1981; Wada et al., 1985; Nakamura and Wada, 1985)) or membranes. Membranes are electrically polarized due to different electric potentials generated by the presence of ions of opposite charge on both sides.

2 The real elementary particles, which are present in matter and relevant on biological scale are electrons, protons and neutrons. Yet, quasiparticles are emergent phenomena that occur in complex nanoscale systems and behave as if the systems contained (fictional) particles. Contrary to modeling with coupled elementary particle types, the theoretical work with quasiparticles is very useful since both, the mathematical formalism and the physical understanding significantly simplify the description of field-related phenomena (but limited only to those).


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To summarize, the basic idea is that the metabolic energy induces vibrations in electrically polar molecules, which, in turn, then generate a cellular electrodynamic field. The following section reviews the most important works that can be categorized under this idea.

Figure 1. Working model of the generation of a cellular electrodynamic field. Vibrational (phonons – heat) energy from several metabolic sources is supplied to microtubules and membranes to excite their electrically polar vibrations. These vibrations are expected to work in nonlinear regime (e.g. due to strong static electric field from mitochondria) which allows for energy exchange among frequencies (vibration modes) and other properties – see text. Organized water surrounding biological structures is expected to cause lowered damping, thus increased coherence, of the vibration modes compared to bulk water. Frequencies of the biological electrically polar vibrations and of thereby generated electromagnetic field are most likely lying in the range kHz– THz.

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Free ions within the cell and electrons/polarons in biomolecules are able to oscillate in kHz–THz region (up to only MHz for ions). However, the mechanism of how the metabolic energy can excite oscillations of biomolecular electrons/polarons and free ions in these frequency regions haven’t been analyzed yet.

Fröhlich's theory In 1968, Herbert Fröhlich postulated that biological systems exhibit coherent longitudinal3 vibrations of electrically polar structures (Fröhlich, 1968a,b, 1969). In order to fit into the Fröhlich’s model, a system has to fulfill the following necessary conditions: • electric polarity • vibration modes in radiofrequency / THz region • sufficient energy supply • nonlinearity Electrically polar structures contain electric charges. When they vibrate, they become able to generate electrodynamic fields. The original Fröhlich model was general and as such did not limit this process to any particular cellular structure. From his model it follows that when the energy supply exceeds a critical level, then the polar structure will enter a condition in which a steady state of nonlinear vibration is reached. This would, furthermore, result in energy storage of highly (coherent) ordered fashion in single or few degrees of freedom. This order expresses itself in a long-range phase correlation, which is physically similar to superconductivity and superfluidity, where the behavior of particles is communal and inseparable. The energy source in this model is metabolic energy, and the nonlinearity4 of the vibrating system is caused by a strong static electric field. The existence of very strong static electric fields in the cell membrane led Fröhlich to consider cellular membranes as the source of the postulated vibrations. Fröhlich’s model created much enthusiasm in the scientific community. Based on his theory, it was predicted that the biomolecular elctrodynamic field would appear in the range of 100 to 1000 GHz. While some researchers

3 Longitudinal vibration modes in matter have been considered by Fröhlich (1969), because they don’t lose energy by radiation (at least in bulk matter) in contrast to transversal vibrational modes as is well known in solid state physics. 4 A nonlinear system is one that does not satisfy the superposition principle, or one whose output is not directly proportional to its input. In the context of Fröhlich’s model it is important note that nonlinearity enables transfer of energy between various frequencies, which is not possible in linear systems. In Fröhlich model, nonlinearity enables channeling (condensation) of energy into one or few modes (frequencies).


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used Raman spectroscopy to probe for vibrations in the predicted frequency region and reported results apparently confirming the nonthermal vibrations predicted by Fröhlich (Webb et al., 1977; Webb, 1980; Drissler and Santo, 1983; Drissler and MacFarlane, 1978; Del Giudice et al., 1985), others criticized these results as being an artifact (Layne et al., 1985; Layne and Bigio, 1986; Furia and Gandhi, 1984, 1985; Cooper and Amer, 1983). Ever since its appearance, Fröhlich’s model continued to inspire studies and models that were addressing his original theory (for review see (Fröhlich and Kremer, 1983; Fröhlich, 1988; Pokorný and Wu, 1998; Cifra et al., 2011a; Reimers et al., 2009)). Even though highly skeptical authors (Reimers et al., 2009) admit to a certain extent the feasibility of his theory, it is not widely accepted that processes as described in Fröhlich’s model are really happening in living cells. This is so because the available experimental evidence from studies with biological systems is controversial. Anyone interested in a good and brief description of Fröhlich’s theory may read the article (Šrobár, 2012a) where the model is explained in a clear and exact language.

Figure 2. Transformation of food to energy which can (i) perform work (via ATP), e.g., in terms of protein motion, (ii) induce vibrations and (iii) heat. Note that heat can be also understood as a broad frequency spectrum of vibrations and, further, that oxidative metabolism includes mitochondria-dependent heat generation.

Microtubules After the discovery of the cytoskeleton in 1970s, microtubules (MTs) became a serious candidate for being sources of cellular electrodynamic fields. This was due to the fact that MTs fulfill the requirements needed for a Fröhlich system and to generate of electrodynamic fields. Nowadays, microtubules are considered not the only possible candidates but most probable and most widely studied ones.

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Microtubule structure and electric polarity MTs have a well-known and accepted structure, composed of tubulin heterodimer subunits that are electrically highly polar (Mershin et al., 2004; Tuszynski et al., 2002). MTs resemble hollow tubes (Dustin, 1984) whose growth (driven by tubulin polymerization) is nucleated by centrosomes or other microtubule organizing centers. The electric polarity of tubulin heterodimer was predicted from its atomic structure (Mershin et al., 2004; Tuszynski et al., 2002) and was also probed in several experiments (Mershin et al., 2004; Schuessler et al., 2003; Böhm et al., 2005).

Energy supply to microtubules MTs in vivo are characterized by their perpetual alternation between growth (tubulin polymerization) and shrinking (MT depolymerization). This dynamic instability results from a constant influx of energy via the assembly and then followed by the disassembly of GTP rich tubulin heterodimer subunits (Caplow et al., 1994; Caplow, 1995; Caplow and Shanks, 1996). A further energy supply to MT vibration is assumed to come as a fraction of energy used for the movement of motor proteins aligned with MTs. Finally, the energy that is dissipated from mitochondria may also translate into vibrational MT-movement resulting in the generation of an electrodynamic field (Pokorný et al., 2008; Cifra et al., 2010). Mitochondrial ATP production by the citric acid cycle has an efficiency of ca. 40%. The remainder of the energy usually dissipates as infrared vibrations as well as infrared and optical (Hideg et al., 1991) radiation. In short, the efflux of energy from the mitochondria represents the most significant source of energy which may lead to the excitation of MT vibrations. The amounts of energy generated by the above-mentioned processes are well described in the literature. The open question is if this energy can actually excite vibrations of microtubules or other structures without immediate dissipation into heat.

Nonlinearity of microtubule vibrational dynamics Mitochondria were also found to be sources of strong static electric fields, namely in the range of 106 V/m, presumably due to the creation of a proton gradient. This static electric field of mitochondria penetrates up to a few micrometers into the cytosol (Tyner et al., 2007). At first sight, this is a controversial finding because in ionic solutions the static electric field should be effectively screened by counterions within few Debye lengths, i.e. a few nanometers. Yet, some authors argue (Tyner et al., 2007) that the simple ionic solution is not a proper model for intracellular water and, instead that a complex fluid and gel-like model where the ion-mobility is hampered reflects experimental reports much better (Zheng and Pollack, 2003; Zheng et al., 2006; Pollack et al., 2006).


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Most interesting here is the regularly found alignment of mitochondria along MTs. It is expected that the vicinity of the two structures combined with their electric properties lead to nonlinear electrodynamics of MT (Šrobár, 2009; Šrobár, 2012b) as the strong electrostatic fields of mitochondria shifts the vibrations of the microtubules to a nonlinear regime. It is the nonlinear regime in Fröhlich’s theory that enables the excitation of polar vibrations of molecules above their thermal level so that an electrodynamic field around them can be generated.

Vibrations of microtubule and their damping Microtubules are theoretically predicted to display collective vibrations in the regions between kHz and GHz (103–109 Hz) region (Sirenko et al., 1996; Gu et al., 2009; Wang et al., 2009; Deriu et al., 2010). The excitation of MT vibrations were the mainstay of the model that was proposed by Pokorný (Pokorný et al., 1997; Pokorný 1999; Pokorný et al., 1998) who analyzed the longitudinal vibrations with slip boundary conditions: he concluded from his calculations that vibrations of microtubule should not be not overdamped. Some scientists raised doubts about the possibility of his theory because they assumed that a viscous cytosol should dampen any vibrating cytosolic organelles (Foster and Baish, 2000; Adair, 2002). The cytosol could have a dampening effect on organelle vibrations if there was a ”noslip” boundary condition between cellular structure and the surrounding cytosol. However, it was argued (Pokorný , 2003, 2005) that lowered mobility of ions in the cytosol results in a ”slip” between microtubules, their adjacent ionic layers and the cytosol, making microtubule vibrations in the cytosol physically plausible. Even though there are further arguments for the plausibility of underdamped microtubule vibrations in vivo (Pokorný et al., 2011) the actual quality factor of microtubule vibration modes remains still an open question demanding careful spectroscopic studies. Only two pioneering published experimental studies on microtubule vibration are currently available, but none of them deliver an estimation of the quality factor of MT oscillations from measured data (Hameroff et al., 1986; Pizzi et al., 2011). To conclude this review about the feasibility of microtubule oscillations, the very interesting findings of A. Bandyopadhyay on microtubules should be mentioned. His team performed recent experiments, which go beyond the study of vibrational properties of microtubules and include also electronic properties, which are more generally described in subsection 3.2.3. His results suggests that microtubules manifest (i) resonant-like response of DC conduction to specific applied radiofrequencies (ii) Fröhlich-like condensation (iii) coherent radiofrequency emission after pumping with radiofre-

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quency signal and other intriguing features (see Bandyopadhyay, 2011, 2012; Sahu et al., 2013a,b).,

Vibrations of other cellular structures Technically, any cellular structure or substructure can oscillate at its resonance frequency – eigenfrequency when excited by energy unless strongly damped. For example, Smith calculated that a spherical cellular membrane has a mechanic resonance frequency of 1010 Hz (10 GHz) (perpendicular to the membrane surface) and a mechanical circumferential resonant frequency of 108 Hz (100 MHz) (parallel to the membrane surface); the electromagnetic resonance of the cell membrane (again parallel to the membrane surface) occurs at a frequency of 1013 Hz (10 THz) (Jafary-Asl and Smith, 1983). Weak resonances in the region around 36-38 GHz have also been detected on erythrocyte ghosts in suspension (Blinowska et al., 1985). This result has been attributed to the vibration modes of the cell membrane which roughly fit the prediction of Smith (Jafary-Asl and Smith, 1983).

3.2.2. Ionic oscillations An electrochemical model was proposed by Pohl where he suggested that electrodynamic fields can be generated within the cells by the coupling of oscillating chemical reactions with physically mobile ions, finally leading to charge waves (Pohl et al., 1981; Pohl, 1982). In his model, the oscillations of ions can be induced by chemical reactions, where the direction of oscillations will be steered by filamentous cellular structures. Pohl’s model for the generation of cellular electrodynamic oscillations has not been developed further. Since many types of chemical reactions generate also sound emission with spectra up to 1 MHz (Betteridge et al., 1981; Wentzell and Wade, 1989), oscillatory chemical processes up to this frequency cannot be excluded. However, the current author does not know about the existence of periodic high frequency chemical oscillations that come from biologically relevant models.

3.2.3. Electronic oscillations One of the necessary conditions for kHz - THz electronic oscillations in biomolecules is their electronic conductivity. One biomolecule that is known to conduct electrons is DNA (Fink and Schönenberger, 1999; Abdalla, 2011). One speaks (for DNA) of a so-called phonon assisted conductivity attributed to polarons (Conwell and Rakhmanova, 2000; Endres et al., 2004; Henderson et al., 1999), which are quasiparticles that involve charge (here electron) and associated deformation of the lattice (cloud of phonons). The DNA polaronbased conductivity is now a widely and intensively studied scientific field. Due to these conductive properties, a collective of authors labels DNA as an antenna for electromagnetic fields (Blank and Goodman, 2011).


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While proteins were for a long time generally accepted to be non-conducting (Kertesz et al., 1977), some theoretical predictions propose conduction or semiconduction to occur in them (Szent-Gyorgyi, 1941; Cope, 1973). Indeed, there is strong current evidence that metaloproteins enable enhanced electron transfer (Gray and Winkler, 2005). It has also been shown that aromatic amino acids, such as tryptophan, promote electron conduction (Shih et al., 2008). There is, furthermore, a very recent example of semiconduction of a metal-reducing bacterial polypeptide named geopilin (Reguera et al., 2005; Veazey et al., 2011; Feliciano et al., 2012) found in several types of bacteria (Gorby et al., 2006), which led the authors to propose that conductive bacterial polypeptide nanowires represent a common bacterial strategy for efficient electron transfer and energy distribution. The other theory of biological charge conduction and electrodynamic generation relates to electrosoliton5. Electrosolitons can be viewed as a quasiparticles involving electrons that could provide transport of charge in biological systems and were considered as an important contender of electrodynamic field generation in the microwave frequency region (Brizhik and Eremko, 2003; Brizhik, 2003; Brizhik and Eremko, 2001; Musumeci et al., 2003). These works have been inspired by a seminal work of Davydov who theoretically predicted the existence of solitons in proteins, α-helixes (Davydov, 1979), although his idea was originally dealing with soliton of zero total charge (exciton). Physically, there is a tight relation between electrosoliton and polaron (Brizhik and Eremko, 2003), because they both involve charge and interact with lattice vibrations (phonons). However, for a soliton to appear, non-linear interactions within the lattice have to occur (Cantu Ros et al., 2011). Although other types of solitons (optical, water waves) are perfectly accepted to exist and their properties are being technically exploited in physics, there is still no clear direct and broadly accepted experimental evidence for Davydovs solitons or electrosolitons to exist in biological systems (Austin et al., 2009) and there are ongoing theoretical debates whether it can exist at all (Lomdahl and Kerr, 1985; Xiao, 1998). Studies in this subsection indicate feasibility of electron conduction in biomolecules. Polaron conductivity is well accepted in DNA and is an ongoing research question in the case of proteins. However, the further two fundamental questions remain for the feasibility of electronic oscillations in biomolecules: • How can the metabolic energy input result in collective excitation of electron/polaron oscillations? Could such oscillations, give rise to an electrodynamic field An electrosoliton is an electrical counterpart of a soliton. Soliton is a self-reinforcing solitary wave (a wave packet or pulse) that maintains its shape while it propagates.

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• Do electron/polaron oscillations exhibit lower damping than electrically polar vibration states (as mentioned in section 3.2.1)?

4. Experimental evidence for cellular electrodynamic fields There is an accumulating evidence for the necessary conditions for generation of cellular electrodynamic field as described in section 3.2. However, apart from various indirect evidence there exist just several pioneering works on direct experimental detection of cellular electrodynamic activity.

4.1. Indirect cellular EMF detection by dielectrophoresis An electric oscillation can be detected indirectly using a technique called dielectrophoresis (DEP) (Pohl, 1978). In this technique, electric oscillations are detected as effects of a non-uniform electric field on a neutral particle via a polarization force. One of the pioneers in measuring cellular electrodynamic fields using the DEP method was Herbert A. Pohl (Pohl, 1980b,a, 1981; Roy et al., 1981; Pohl et al., 1981; Pohl, 1982, 1983; Rivera et al., 1985). In the DEP method, the electric field induces a dipole moment in sample particles and the resulting force acting on them is the force of an electric field on a dipole. Since Pohl used small particles of a few micrometers in size to probe cellular electric oscillations, he often used the term “micro-DEP” (µ-DEP). In this method, particles were either repelled from or attracted to the surface of cells depending on whether particles had a lower dielectric constant (BaSO4, SiO2, Al2O3) or higher dielectric constant (BaTiO3, SrTiO3, NaNbO3) than the suspending medium, which was usually water-based. Pohl estimated that the frequencies of cellular electrical oscillations were in the radiofrequency range (5 kHz to 9 MHz) (Pohl, 1980b; Pollock and Pohl, 1988). In his experiments, he tested several types of cells such as bacteria, fungi, algae, nematodes and mammalian cells, all of which showed, under suitable conditions, a dielectrophoretic effect interpreted to be caused by a cellular electrodynamic field (Pollock and Pohl, 1988). Other investigators reported similar findings for diverse cell types including human leukocytes (Pohl and Lamprecht, 1985; Hölzel, 1990, 2001; Pokorný, 1990; Jandová et al., 1987).

4.2. Indirect experimental evidence for cellular kHz–GHz oscillations through effects of external fields There is large body of experimental work (a few hundreds) on the external electromagnetic field resonance effects on (at specific frequencies) biological systems, (for a review see Cifra et al., 2011a; Belyaev, 2005a,b). Especially Russian authors (Betskii et al., 2000; Devyatkov, 1973) interpreted these results as a proof of internal cellular electrically polar vibrations


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being affected by external fields. The idea was that such resonant effects are possible only if there are structures in the cell which are able to vibrate with high quality factor at the same frequencies as those applied externally, i.e. resonate. Their following argumentation was that if there is a cellular structure able to resonate (oscillate) with an external electromagnetic field, then it is able to generate electromagnetic oscillations under the condition that (metabolic) energy is supplied, see Golant, 1989a,b and Devyatkov et al., 1991, p.66 for original Russian texts and Golant (1994); Betskii et al. (2000) for English texts. However, the resonant biological effects of electromagnetic fields can also be explained by other, though more complex, mechanisms such as (i) influence of field on triplet free radical chemistry (Keilmann, 1986), (ii) hydrodynamic flow due to inhomogeneous surface heating of the water-like biological samples (Khizhnyak and Ziskin, 1996) and due to hypothesised oscillations of water molecule polymers (Sinitsyn et al. (2000)).

4.3. Direct electronic detection Already some work aimed at the direct electronic detection of electrodynamic cellular signals has been done (Table 4.3). The first direct evidence for electrodynamic field generation in the spectral region of kHz–GHz by cells was attempted to be obtained in a series of experiments that used direct electronic detection from a single cell or a suspension of cells. Using a spectrum analyzer, Jafary-Asl and Smith claimed to find electrodynamic signals emitted from Saccharomyces cervisiae in the range of 7–80 MHz (Jafary-Asl and Smith, 1983; Del Giudice et al., 1989). Later on Rivera and Pohl (Pohl and Pollock, 1986) detected a spectrum of signals from the alga Netrium digitus with peaks around 7 and 33 kHz. But Hölzel who extensively analyzed the frequencies of different groups of cells in the MHz region (Hölzel, 1990; Hölzel and Lamprecht, 1995, 1994; Hölzel, 2001) disagreed with Jafary-Asl and Smith claiming that the frequencies they had reported were mainly artifacts probably due to a positive feedback coupling in the amplifier. However, with improvement in detection techniques other researchers claimed to successfully detect cellular electrodynamic fields, e.g., during the process of mitosis of yeast cells, in MHz region (Jelínek et al., 1999, 1996; Pokorný et al., 2001).

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Organism

Frequency or wavelength

References

Netrium Digitus (Algae)

7 kHz, 33 kHz

(Pohl and Pollock, 1986)

Saccharomyces cerevisiae (yeast)

0.4–1.6 kHz

(Jelínek et al., 2009; Cifra, 2009)

1, 7, 50 (60)–80 MHz

(Jafary-Asl and Smith, 1983; Del Giudice al., 1989)

8-9, 8.2 MHz

(Jelínek et al., 1999, 1996; Pokorný et al., 2001)

1.5, 2.6, 5.7, 18, 52 MHz

(Hölzel, 1990; Hölzel and Lamprecht, 1995, 1994; Hölzel, 2001)

42 GHz (attempts only, not considered significant)

(Jelínek et al., 2002, 2005, 2007; Kučera, 2006)

Schizosaccharomyces Pombe (yeast)

3.1, 4.8 MHz

(Hölzel, 1990; Hölzel and Lamprecht, 1995, 1994; Hölzel, 2001)

frog gastrocnemius muscle (electrically stimulated)

0.2–2 mm

(Gebbie and Miller, 1997)

electrically stimulated nerve from blue crab Callinectes sapidus

3–10 µm

(Fraser and Frey, 1968)

Table 1. Direct electronic detection of electrodynamic cellular signals up to the THz region. Indirect detection of cellular electrodynamic fields, for instance by its dieletrophoretic effect, is not included.

Statistical analysis revealed four peaks in detected power during the mitosis. It was suggested that these peaks of the cellular electrodynamic activity can be related to the microtubules reassembling into the mitotic spindle, with binding of chromatids to kinetochore microtubules, and with elongation of mitotic spindles during anaphase A and B (Pokorný et al., 2001). Experiments aimed at the detection of cellular electrodynamic activi-


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ty in the region around 42 GHz (Jelínek et al., 2002, 2005, 2007; Kučera, 2006) has been carried out with very limited success. A recent review (Kučera et al., 2010) elucidates reasons for the limited success of experiments on the direct electronic detection of cellular electrodynamic field. Practically all hitherto used measurement systems haven’t fulfilled at least some necessary technical requirements which stem from identified and predicted biophysical properties of cellular electrodynamic sources. Such technical requirements include mainly nanoscopic resolution of sensor and suitable input electrical characteristics of preamplifiers. This was caused by the ignorance of the early authors on the one hand and also by technological limits of that time on the other hand.

5. Conclusion The research of high frequency (kHz–THz) cellular electrodynamics has a 40 years long history. As the initial enthusiasm to seriously test early theories has been hindered by technological limitations, this research field had a rather slow scientific evolution. Yet, current technology together with basic physical concepts allowed identification of cellular structures and processes that could give rise to a cellular electrodynamic field. What is needed now is to establish if there is really any nontrivial specific role, i.e. the biological relevance of cellular and biomolecular electrodynamics. As electrodynamic fields do have the property to act on charged structures and as exactly such charged structures cause these fields, we can assume that there exists a feedback system between the charged structures and the field. This, however, is of great significance because it induces the possibility of an electrodynamic contribution to the organisation of molecular cell processes. We see several experimental indications that biological electrodynamic fields may mediate the interaction among biomolecules and biosystems. However, the development of bioelectro-dynamics bears also a new understanding of physical interactions in biology presumably not only for the smallest scale of biomolecules but up to the scale of multicellular organisms. Finally, if the hypotheses of a) underdamped electronic/electrically polar mechanical oscillations in microtubules and other biomolecules, which would be measureable with new generation of sensors and b) the biological significance of these oscillations in biomolecular reaction rate and e.g. further in mitosis or cell adherence will be confirmed, the future applications of bioelectrodynamics could lead to the controlled development of new noninvasive diagnostic methods and therapies based on electromagnetic fields and modification of biomolecules, the substrate of endogenous biological electrodynamic fields.

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6. Acknowledgements Author acknowledges financial support from Czech Science Agency, projects n. P102/10/P454, 15-17102S and P102/11/0649. Discussions with J. Pokorný, F. Šrobár, J. Proška and D. Fels are deeply appreciated. O. Kučera is acknowledged for discussions and preparation of figures.

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Chapter 11

Investigating encounter dynamics of biomolecular reactions: long-range resonant interactions versus Brownian collisions 1

1,2

2

2

Jordane Preto , Ilaria Nardecchia , Sebastien Jaeger , Pierre Ferrier 1* and Marco Pettini 1

Centre de Physique Théorique – CNRS UMR7332; Aix-Marseille University, Marseille, France Centre d’Immunologie de Marseille-Luminy (CIML), Aix-Marseille University; Institut National de la Santé et de la Recherche Médicale (Inserm), U1104; Centre National de la Recherche Scientifique (CNRS), UMR7280; Marseille, France 2

Abstract. Self-organization of living organisms is of an astonishing complexity and efficiency. More specifically, biological systems are the site of a huge number of very specific reactions that require the right biomolecule to be at the right place, at the right time. From a dynamical point of view, this raises the fundamental question of how biomolecules effectively find their target(s); in other words, what forces bring all these specific cognate partners together in an environment as dense and ionized as cellular micro-environments. Here, we investigate the possibility that biomolecules, besides traditional Brownian motion, interact through long-range electromagnetic interactions as predicted from first principles of physics; long-range meaning that the mentioned interactions are effective over distances much larger than the typical dimensions of the molecules involved. Correspondence/Reprint request: Dr. Jordane Preto, Centre de Physique Théorique – CNRS UMR7332; Aix-Marseille University, Marseille, France. E-mails: [email protected] and *[email protected]

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Introduction Progress in molecular and cellular biology is consistently linked to a better knowledge of the structure of, and functional interplay between, biomolecules such as DNA, RNA and proteins. Even though these building blocks of the living matter display no apparent systematic order, it is well known that all the relevant biochemical processes follow a precise time schedule, in other words display a dynamical order. Far from having anything to do with a chemical reactor, living cells host chemical reactions catalyzed by enzymes whose critical action accelerates by orders of magnitude the reactions rates of biomolecules via lowering of the free energy barrier (i.e. making them realistically feasible) (Garcia-Viloca et al., 2004). Likewise, DNA/RNA-interacting proteins (e.g., helicases, polymerases, nucleases, recombinases) modulate essential transaction processes involving nucleic acids to achieve DNA duplication and repair, gene expression and recombination, with an astonishing efficiency. Such an astonishing efficiency raises a fundamental question from a physical point of view. With biochemical reactions mostly being stereospecific, two (or more) reacting partners have to come in close contact and exhibit a definite spatial orientation to initiate particular reactions. Then, how do the various actors in a given biochemical process efficiently find each other (i.e., how does a protein effectively recruits the appropriate co-effector partner(s) or selectively connects with its DNA/RNA target(s) in a crowded cyto/ nucleoplasm environment)? In other words, what kind of physical forces bring all these players at the right place, in the right order and in a reasonably short time to sustain cellular function and ultimately cellular life? The classical way to tackle these issues invokes Brownian motion. At physiological temperature, ubiquitously distributed water molecules undergo chaotic motion, colliding with larger/heavier fluid components. On the latter, the neat outcome from simultaneous hits is a force of both random intensity and direction. Hence, large molecules move in a diffusive way throughout the cellular spaces and sooner or later shall encounter their cognate partners. Is this truly a good answer to the problem formulated here? Many doubts arise when one tries to estimate diffusion driven activation for some of the biochemical processes mentioned above. In particular, it turns out that proteins are capable of finding their cognate partner 10 and even 100 times faster than predicted by Brownian diffusion rates (Stroppolo et al., 2001). On the basis of these results, many authors surmised that electrostatic effects could critically affect the time needed by two biomolecular partners to meet each other. Yet, while electrostatic effects are dominant only over very short distances, this is not the case for time varying fields; they might have (regarding typical molecular dimensions)

Encounter dynamics of biomolecular reactions

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influence over very long distances. Let us sketchily discuss below why this is so.

1. Electrostatic and electrodynamic intermolecular interactions Typical biomolecules (e.g., proteins, nucleic acids) are electrically charged and have non-vanishing dipole moments 1. So it is natural to consider the possible role that might be played in the above-mentioned dynamical organization by electrostatic intermolecular interactions. On this point, it should be stressed that significant progress has been made understanding interactions acting at a short distance, i.e. at a distance comparable with the typical size of biomacromolecules (around 50 Å for typical proteins) or shorter (Cherstvy et al., 2008). But electrostatic interactions can principally also act at a long distance like in dipole-dipole or Coulomb interactions and, hence could a-priori play a role in the dynamical organization of biomolecular reactions in living matter. However, freely moving ions in intracellular water (cytoplasm) make the Debye length 2 smaller than 10 Å, shortening the action range of Coulomb and dipole interactions too. Moreover, the static dielectric constant of water, which is ubiquitous in living matter, is very large (~80) at room temperature implying a further reduction of the strength of electrostatic interactions. These are perhaps the reasons why intermolecular interactions acting at a long distance have been hitherto poorly investigated in biology. Nevertheless, though electrostatic interactions between charges/dipoles in the electrolyte of cell cytoplasm are exponentially damped with distance, Debye screening proves generally inefficient for interactions involving oscillating electric fields. In particular, it was experimentally shown that, when acted upon by an electric field oscillating at a frequency larger than ~250 MHz, an electrolyte in physiological-like conditions behaves like a pure dielectric (Fig. 1) (i.e, without conducting properties so that Debye screening – or more specifically skin effects – is no longer effective) (de Xammar Oro et al., 1992; de Xammar Oro et al., 2008). In other words, charges/dipoles oscillating faster than a suitable frequency are not screened by any static electric charges and are thus able to exert long distance forces. In this context, it should be also remarked that high-frequency electric fields are neither shielded by free ions nor weakened by the dielectric constant of water, which, beyond a few hundred of GHz, drops from the value of ~ 80 to about 4 (Ellison, 2007). ______________________________ For example, the dipole moments of protein molecules such as hemoglobin, albumin, myoglobin and lactoglobulin were found to be very large, of the order of 200−1000 Debye units. These values should be compared with the dipole moments of small organic compounds, i.e., 3−5 D.

1

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Figure 1. (a) Scheme of the experimental device to measure the frequency response of an electrolyte (Z) contained between the plates of a capacitor and (b) the impedance Z of the electrolytein experimental setting as a function of the frequency of the applied electric field according to (de Xammar Oro et al., 1992; de Xammar Oro et al., 2008).

All in all, one may legitimately inquire whether electrodynamic interactions do play a sizable role in the organization of biomolecular reactions, for example, as far as attractive interactions (negative potential) are concerned, by facilitating encounters of biomolecular cognate partners over long distances. In this case, this would imply that electrodynamic forces might have resonant properties so that a particular biomolecule would be only attracted by its specific target, and not by other neighbouring biomolecules. At this stage, we should remark that electrodynamic interactions are especially well known in quantum electrodynamics (QED) when occurring between two neutral atoms – or small molecules. In this case, long-range interactions have been shown to arise when one of the atoms is in an excited state, and the transition energies of both atoms are roughly similar, i.e., at resonance (Stephen, 1964; McLachlan, 1964) (offresonance conditions would lead to short-range interactions). This is the condition of exchange degeneracy implying that the atoms are in a quantum entangled state (Stephen, 1964; McLachlan, 1964). However, as entangled states are fragile (with lifetimes estimated around 10–10 s) their persistence over long distances (i.e., larger than 50 Å) in the noisy environment of living matter could be questioned. Therefore, long-range quantum interactions between biomolecules are not very probable. On the other hand, electrodynamic interactions can be well derived classically (Preto, 2012; Preto et al., 2013). In this case, it can be shown, similarly to QED, that electrodynamic interactions are effective at a long-range only in resonant conditions. Here resonance means that the dipole moments of the two


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molecules – due to conformational vibrations rather than electronic motions – oscillate with a common frequency.

2. Physical picture behind long-range resonant interactions For the sake of clarity and to give the reader a rough picture about why frequency resonance can lead to dipolar interactions of a much longer range than off-resonance ones, it is worth recalling that the motion of two interacting molecules with oscillating dipole moments can be generally decomposed as the sum of two uncoupled motions of the interacting system. These collective motions, usually referred to as normal modes, are characterized by their own frequency (normal frequency) whose value, of course, strongly depends on the frequencies of each dipole taken separately. For a system of two dipoles exhibiting the same frequency, it can be shown (Preto et al., 2013) that normal modes correspond exactly to situations where dipoles oscillate in phase (attraction) and out of phase (repulsion) respectively (see Fig. 2). In this case, by favoring one normal mode with respect to the other one, attraction or repulsion (according to which mode has been excited) can be expected to remain between both dipoles during a time much longer than the characteristic period of dipole oscillations. Thus, averaging the interaction energy over this period will eventually result in a net attracting or repulsing long-distance force between the two molecules. Of course, this result is mainly because dipoles oscillate with the same frequency.

Figure 2. Two normal modes of a system of two interacting dipoles A and B oscillating at the same frequency; (1) symmetric mode characterized by attraction between the dipoles (in-phase oscillations), (2) antisymmetric mode characterized by repulsion (out of phase oscillations).

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In the case of two molecules with notably different frequencies, in-phase and out-of-phase motions cannot bemaintained indefinitely. More specifically, it can be shown, when averaging over a period of dipole oscillations, that attractive and repulsive contributions balance exactly at first order, leading to second-order short-range interactions between molecules with amplitude comparable to standard van der Waals interactions of quantum electrodynamics (Preto, 2012; Preto et al., 2013). Coming back to dipolar interactions at resonance, it should be again emphasized that long-range interactions (strong attraction or repulsion) take place only if one normal mode is strongly excited (in other words, the interacting system should be in a quasi-stationary state characterized by phase locking). If not, this might lead to a situation similar to off-resonance where long-range attractive and repulsive contributions balance one another. From a biological point of view, it is thus of particular importance to investigate whether interactions between molecules of living cells can lead to the excitation of a particular collective mode of vibration. This point is discussed in details in the two upcoming sections.

3. Coherent collective excitation in biological systems Long-range interactions between molecules and their possible role in a biological context were originally investigated by Fröhlich (Fröhlich, 1968; Fröhlich, 1977; Fröhlich, 1980). Herbert Fröhlich (1905-1991) was a distinguished theoretical physicist who started his research right after the formulation of quantum mechanics. He made important contributions to many fields of physics but he is mostly famous for providing the first successful explanation of superconductivity as the result of electron-phonon interaction. In the last 24 years of his life he turned to fundamental problems in biophysics developing a new fundamental idea known as "Fröhlich coherence". Fröhlich considered the extraordinary dielectric and polarization properties at the microscopic level of living matter, noting, for example, that the electric field of a cell membrane is in the order of 105 V/cm (very high indeed; and this value can even grow in proximity of proteins, nucleic acids, etc.). A major consequence is that non-linear effects may occur under such physical conditions of the cell membrane. Thus he developed a theory that took into account non-linearity, in particular, he considered that vibrations in polar systems – here the cell membrane – are accompanied by polarization waves generating an electromagnetic field that could mediate a long-range interaction and play the role of an ordering agent. The main idea is as follows: random vibrations are obviously an omnipresent kinetic property of matter, but order can emerge out of disorder if non-linearity is considered. In fact, non-linearity in a system of oscillators enables energy transfer between different normal modes of vibration. Energy may be


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channelled from the high-frequency oscillators downward to the lowfrequency oscillators. Energy supplied to a nonlinear open system is not immediately thermalized, but condenses in certain modes. Typical examples of strong excitation of particular normal modes in open systems are given by the Laser or by the Rayleigh-Bénard convective instability (where only a few degrees of freedom are excited that give rise to macroscopic collective behaviours). On the basis of general principles Fröhlich derived a system of rate equations that describe the time evolution of average occupation numbers in a collection of vibrational modes of model structures such as proteins or membranes. He considered mode-mode coupling, e.g., of two molecules through the individual interaction of each mode with a heat bath. This interaction enables energy transfer between two normal (vibration) modes with different frequencies and the heat bath balances the difference in energy quanta. In that sense, a Fröhlich system displays the (open system) analogue of a Bose-Einstein condensation; since if the energy supply rate (coming from the environment) exceeds some threshold value then almost all the energy is concentrated in the lowest frequency mode of a vibration. In other words, it can be considered that almost all the components of the system synchronize, i.e. oscillate in a collective way, at the lowest frequency of the system’s spectrum.

4. Coherent collective excitations and their relevance to longrange resonant interactions between biomolecules Experimental evidence for the existence of collective excitations within macromolecules of biological relevance is available for proteins (Painter et al., 1982; Chou, 1985; Xie et al., 2002) and for polynucleotides (DNA and RNA) (Painter et al., 1981; Painter et al., 1982; Chou, 1984; Powell et al., 1987; Fischer et al., 2002) through the observation of low-frequency oscillations modes in the Raman and far-infrared spectra of polar proteins. These spectral features are commonly attributed to coherent collective oscillation modes of the whole molecule (protein or DNA) or of a substantial fraction of its atoms, which could be strong enough to be “felt” by other macromolecules from far away despite thermal noise. Applied to biomolecular dynamics, such coherent excitation could give rise to strong dipolar interactions between biomolecules that would be still active wellbeyond Debye length provided that the dipole moments of molecules oscillate with the same frequency, as mentioned above. Moreover, let us remark that Fröhlich estimated on the basis of theoretical arguments (Fröhlich, 1968; Fröhlich, 1977; Fröhlich, 1980) collective vibrational modes of metabolically active biological systems in the frequency range of 0.1– 10 THz. This is in line with spectral features of standard biomolecules

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(Markelz Et al., 2002). In particular, it is seen that the range of relevant frequencies is larger than hundred of GHz, so that corresponding electric fields are hardly affected by the surrounding medium (Debye length and dielectric dispersion, see above). Coming back to the theory of electrodynamic interactions, it should be stressed that Fröhlich emphasized, inter alia, that long-range interactions may occur at resonance even if the system of interacting dipoles is close to thermal equilibrium. Yet, after theoretical investigations and numerical estimates of electrodynamic interactions, we found the above statement to be incorrect: besides resonance, oscillating dipoles must be out of thermal equilibrium to interact effectively over long-distances (Preto, 2012; Preto et al., 2013). More explicitly, using elements introduced in section 2, it turned out that a system of interacting dipoles in thermal equilibrium gives rise to a situation where normal modes of the interacting system, characterized by in-phase and out-of-phase oscillations respectively, contribute the same way to the overall dynamics, hence cancelling out any long-range average effect of dipolar nature. Thus, an out-of-thermal-equilibrium condition, characterized by the strong excitation of a particular normal mode of the interacting system, is required so that net long-range attraction or repulsion can emerge between dipoles. From a biological point of view, the abovementioned result raises a new question; namely, can biomolecules of living cells be subject of such energy redistribution in the space of modes? A possible answer is again provided by Fröhlich's theory (Fröhlich, 1968). In fact, the above reported condensation phenomenon, which is characterized by the emerging of the lowest frequency mode that contains nearly all the energy supply from the environment, can also be seen as an out of equilibrium energy distribution among normal modes of two interacting molecules. In biological systems, the environmental energy supply could be attributed to metabolic energy stemming, for example, from the hydrolysis of adenosine triphosphate (ATP) or guanosine triphosphate (GTP) as well as from ion collisions. In fact, recent works show that the presence of certain ions can enhance the rate of some biomolecular reactions, thus suggesting that these ions – through their collisions on specific sites of the concerned biomolecules – transfer external energy (Pingoud et al., 2009). On the other hand, it goes without saying that the mode of lowest frequency always corresponds to a configuration of least energy, which, in the case of two interacting polar molecules, is the mode characterized by a strong attraction (in-phase oscillations) as mentioned in section 2. Thus, if a mechanism such as Fröhlich condensation (or more generally dynamic synchronizations) between molecules turns out to be effectively active in biological systems, this could imply long-range attractive interactions between biomolecules provided that the latter share common frequencies in their vibration spectra (Fig. 3).


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Figure 3. Scheme of two oscillating dipoles A and B in water. When both dipoles oscillate with a same frequency ωA ≈ ωB, attractive or repulsive long-range interactions may set in provided that a normal (collective) mode of vibration of the interacting system is strongly excited; see Refs. (Preto, 2012; Fröhlich, 1977).

It is speculated that a variety of biomolecules share similar frequencies with their specific cognate partner (due to specific conformational analogies or complementarities that are crucial so that the chemical reaction actually takes place), which would result in lowering the encounter times between such molecules and thus in increasing the efficiency of particular biochemical reactions. Table I presents a list of observed low-frequency modes in Raman spectra of different proteins. Characteristic features are also present in cells: some studies on Raman spectra of bacterial and mammalian cells revealed that metabolically active systems exhibit many Raman lines in the range of 0.1– 10 THz, whereas these resonances were not observed in resting cells (Webb et al., 1977). Since a large range of chemical reactions of biological relevance are to be expected for metabolically active cells in comparison to resting ones, such a result might be consistent with the presence of resonance effects influencing the encounter dynamics of biological cognate partners 3. Rowlands reported of effects possibly coming from long-range interactions of the Fröhlich type. In particular, he reported that red blood cells, i.e., erythrocytes (Rowlands et al., 1981; Rowlands et al., 1982) tend to array themselves in stacks, called rouleaux. Rowlands (Rowlands, 1983) found attractive forces to become apparent when red cells were at a mutual distance of about 4 µm apart or less. ______________________________ Note that these finding are also supported by the measured value of the ratio between the intensities of Stokes and anti-Stokes lines (R ≈ 1) very different from the value which should be observed in oscillating systems in thermal equilibrium (R ≈ 0.55) (Webb, 1980).

3

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Protein Insulin Ribonuclease A Lysozyme β-Lactoglobulin

Molecular weight

Observed raman line (cm-1)

5800 (monomer)

22

11600 (dimer) 13700

22 Not observed

14000

25

18000 (monomer)

25

36000 (dimer)

25

α-Chymotrypsin

22600

29

Pepsin

35000

20

Ovalbumin

44000

22

Concanavalin A

55000

20

Table I. Observed Raman low-frequency mode in Raman spectra of standard biomolecules (Painter et al., 1981; Painter et al., 1982; Chou, 1984; Powell et al., 1987; Fischer et al., 2002).

Interestingly, the interaction between the cells is weakened or disappears, either when the cells are deprived of metabolic energy stores, or when their membrane is disorganized, or even when the quasi-static membrane potential is considerably lowered. Another possibly relevant consequence of Fröhlich's coherence theory concerns (the still open) topic of interacting weak electromagnetic fields (EMF) within biological systems. In fact, Fröhlich’s work inspired some experiments on the influence of millimeter waves EMF (10–100 GHz) on biological systems. We cite the works of Grundler and Keilmann (Grundler et al., 1977; Grundler et al., 1983) on growth of yeast cells irradiated with an EMF of 42 GHz with effects on cell division rates and compared to the nonirradiated control as well as the work of Belyaev et al. (Belyaev et al., 2000) on a resonant like dependence of chromatin conformational state of E. coli irradiated with a weak EMF with a frequency around 52 GHz. Possible explanations of these resonant-like effects of external EMF concern conformational effects on biomolecules which, in turn, would modify the endogenous fields generated by their low-frequency vibrations in the cellular environment.

5. Experimental feasibility of a direct detection of intermolecular long-range interactions In spite of the above mentioned results, Fröhlich's ideas as well as the hypothesis that electrodynamic long-range interactions could be relevant to understand the complex organization of living matter at the molecular level


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have, however, suffered from lack of clear-cut experimental evidence and, sometimes, from contradictory results. As a consequence, the approach has lost its attractiveness and sometimes has even been discredited. Moreover, the idea of a simple co-resonance on a single common frequency in the vibrational spectra of interacting molecules seems oversimplified, though justified for a preliminary investigation. Thus, what is the reason to put it forward again? First of all, long-range electrodynamic interactions have been experimentally induced between microscopic dielectric objects (plastic spheres of 1.43 µm diameter suspended in water) by means of intense optical fields (Burns et al., 1989) to excite their dipole oscillations. So, why dipole oscillations due to conformational vibrations of macromolecules should not do the same job? Then, another reason for a renewal of interest in this topic is due to a recent assessment of the feasibility of a yet unexplored strategy to experimentally tackle the question of a possibly active recruitment at a large distance of cognate partners of biomolecular reactions (Preto et al., 2012). It turns out that the present availability of advanced experimental techniques allows coping with an almost direct detection of these putative long-range interactions. Likewise, a feasibility study was based on simplified one-dimensional models describing the dynamics of the approach of a molecule A to a molecule B either under the condition of a random force only or the condition of a random force plus a deterministic long-range attractive force. The aim was to work out the orders of magnitude of the relevant physical parameters of a basic experimental situation, with the latter referring to a solution where a biochemical reaction takes place (Fig. 4).

Figure 4. Molecules A and B are initially placed at a distance x. When they get closer than d they react together.

The results of the analytic computation of the average encounter time between A and B in the two mentioned conditions are given in Fig. 5, where the average encounter time is reported as a function of the initial distance x. The physical values of x are considered in the interval 10–1000 nm corresponding to easily accessible concentrations C of molecules in laboratory experiments, that is, in the range 1 nM–1 mM (x is estimated as C–1/3).

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Figure 5. The average encounter time between molecules A and B is reported versus their initial speraration x. Dotted curves stand for asymptotic behaviours stemming from theoretical predictions (Preto et al., 2012).

The other physical parameters that enter the model (such as temperature, viscosity of the medium, hydrodynamic radii, molecular weights, and so on) are chosen as values typical for average sized proteins. The resulting mean encounter times – obtained with a given set of parameters – vary in the interval of hundred of milliseconds for x ~ 1 µm down to the microsecond for x ~ 700 Å. For the latter value of x, a purely random encounter time exceeds by four orders of magnitude the encounter time in presence of long-range attractive potential of the kind U(r) ~ - 1/r3. The distance at which noticeable differences between encounter times of Brownian molecules and those of molecules driven by a long-range attractive force could be observed, may vary significantly depending on the actual value of a yet free parameter of the resonant potential.

6. Conclusions We have addressed the longstanding problem of long-range recruitment of biomolecular reaction partners in living matter. The astonishingly efficient organization of the complex network of biochemical reactions inside the cell seems hardly understandable in terms of purely random encounters of biomolecules. On the other hand it seems unavoidable that the oscillating dipole moments of macromolecules activate (because of resonance) mutual and selective attraction forces as is predicted by the first principles of electrody-


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namics. However, the lack of compelling experimental evidence has hitherto hindered substantial development of according ideas and intuitions that were put forward by Herbert Fröhlich more than forty years ago. Thanks to powerful experimental techniques nowadays available, it becomes possible to investigate whether the encounters among interacting macromolecules are driven by Brownian diffusion or are accelerated by the presence of attracting forces: an ongoing collaboration between the Theoretical Physics Center (CPT) in Marseille and the Center of Immunology Marseille Luminy (CIML) is addressing this question thoroughly from both theoretical and experimental viewpoints. Preliminary experiments have already given very encouraging results.

Acknowledgements The authors acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme (FP7) for Research of the European Commission, under the FETProactive grant agreement TOPDRIM, number FP7-ICT-318121. Pierre Ferrier laboratory is supported by institutional grants from Inserm and CNRS, and by grants from the Commission of the European Communities, the ‘Agence Nationale de la Recherche’ (ANR), the ‘Institut National du Cancer’ (INCa), the ‘Association pour la Recherche sur le Cancer’ (ARC), the ‘Fondation pour la Recherche Médicale’ (FRM) and the ‘Fondation Princesse Grace de la Principauté de Monaco’.

Bibliography Belyaev, I., Shcheglov, V., Alipov, E. & Ushakov, V. 2000. Microwave Theory and Techniques, IEEE Transactions 48: 2172-2179. Burns, M.M., Fournier, J.M. & Golovchenko, J.A. 1989. Physical Review Letters 63: 12331236. Cherstvy, A.G., Kolomeisky, A.B. & Kornyshev, A.A. 2008. The Journal of Physical Chemistry B 112: 4741-4750. Chou, K.C. 1984. Biochemical Journal 221: 27-31. Chou, K.C. 1985. Biophysical Journal 48: 289-297. de Xammar Oro, J. R., Ruderman, G., Grigera, J. R. & Vericat, F. 1992. Journal of the Chemical Society, Faraday Transactions. 88: 699-703. de Xammar Oro, J. R., Ruderman, G. & Grigera, J. R. 2008. Biophysics 53: 195-198. Ellison, W. 2007. Journal of Physical and Chemical Reference Data 36: 1 Fischer, B.M., Walther, M. & Jepsen, P.U. 2002. Physics in Medicine and Biology 47: 38073814. Fröhlich, H. 1968. International Journal of Quantum Chemistry 2: 641-649. Fröhlich, H. 1977. Rivista Nuovo Cimento 7: 399-418.

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Fröhlich, H. 1980. Advances in Electronics and Electron Physics 53: 85-152. Garcia-Viloca, M., Gao, J., Karplus, M. & Truhlar, D. G. 2004. Science 303: 186-195. Grundler, W., Keilmann, F. & Fröhlich, H. 1977. Physics Letters A 62: 463-466. Grundler, W. & Keilmann, F. 1983. Physical Review Letters 51: 1214-1216. Markelz, A., Whitmire, S., Hillebrecht, J. & Birge, R. 2002. Physics in Medicine and Biology 47: 3797-3805. McLachlan, A.D. 1964. Molecular Phys. 8: 409-423. Nardecchia, I. 2012. Feasibility study of the experimental detection of long-range selective resonant recruitment forces between biomolecules, PhD Thesis, Aix-Marseille University. Painter, P.C., Mosher, L.E. & Rhoads, C. 1981. Biopolymers 20: 243-247. Painter, P.C., Mosher, L.E. & Rhoads, C., 1982. Biopolymers 21: 1469-1472. Pingoud, V., Wende, W., Friedhoff, P., Reuter, M., Alves, J., Jeltsch, A., Mones, L., Fuxreiter M. & Pingoud, A. 2009. Journal of Molecular Biology 393: 140-160. Powell, J. W., Edwards, G. S., Genzel, L., Kremer, F., Wittlin, A., Kubasek, W. & Peticolas, W. 1987. Physical Review A 35: 3929-3939. Preto, J. 2012. Long-range interactions in biological systems, PhD Thesis, Aix-Marseille University. Preto, J., Floriani, E., Nardecchia, I., Ferrier P. & Pettini M. 2012. Physical Review E 85: 041904. Preto, J. & Pettini, M. 2013. Physics Letters A, 377: 587-591. Rowlands, S., Sewchand, L.S., Lovlin, R.E., Beck, J.S & Enns, E.G., 1981. Physics Letters A 82: 436-438. Rowlands, S., Sewchand, L.S. & Enns, E.G. 1982. Physics Letters A 87: 256-260. Rowlands, S. 1983. Coherent Excitations in Blood. In: Coherent Excitations in Biological Systems (H. Fröhlich and F. Kremer eds., Berlin, Springer-Verlag) Stephen, M. J. 1964. Journal of Chemical Physics 40: 669-673. Stroppolo, M. E., Falconi, M., Caccuri, A. M. & Desideri, A. 2001. Cellular and Molecular Life Sciences 58: 1451-1460. Webb, S.J., Stoneham, M.E. & Fröhlich, H., 1977. Physics Letters A 63: 407-408. Webb, S.J., 1980. Laser Raman spectroscopy of living cells, Physics Reports 60: 201-224. Xie, A., van der Meer A.F.G. & Austin, R.H. 2002. Physical Review Letters 88: 018102.



Chapter 12

Synchrony and consciousness 1

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Thilo Hinterberger , Cigdem Önal-Hartmann and Vahid Salari

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1 Section of Applied Consciousness Sciences, University Medical Center Regensburg, Regensburg, Germany; 2Department of Physics, Isfahan University of Technology, Isfahan, Iran

Abstract: Modern neuroscience demonstrates that the emergence of consciousness requires the synchronous interaction of various brain mechanisms which are represented by specific brain areas. Therefore, studying the oscillatory behavior of neuronal networks provides insights into the functioning of the brain in order to fulfill mental tasks that are required to build up the functional frame in which consciousness as a human experience can take place. Thus, an experience of complex contents within one moment in time requires simultaneous activations and information exchange between numerous brain areas. This can be demonstrated in the study of dysfunctions of specific brain regions but also through the analysis of electrical brain oscillations in healthy people. Consciousness however not only requires the brain itself. Moreover, the whole body is involved especially in the production of emotions. Damasio’s theory of emotions is discussed in this context. The interaction between brain and other body processes also requires time-sensitive signal-flows. The coherence between heart and brain and possible synchronicities between biophoton emission in body parts and brain electrical processes are questions in ongoing research. Further, possible synchronicities between consciousness-related body processes and phenomena outside the body are discussed. It is still questionable whether the brain waves interact with the electromagnetic field of the Schumann resonance, i.e. the natural resonance of the geosphere that vibrates with about 8 Hz, a frequency that is within the range of the brains theta waves. Finally, research trying to uncover possible synchronicities in distant brains of closely related people is presented that focuses on the question whether telepathic phenomena can be measured. To summarize it can be stated that synchronicities between body functions especially brain processes are relevant for the neuronal basis of phenomena related to consciousness. However, the influence of electromagnetic phenomena outside the brain on brain states still remains speculative and needs further research.

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Thilo Hinterberger et al.

Correspondence/Reprint request: Dr. Thilo Hinterberger, Section of Applied Consciousness Sciences, University Medical Center Regensburg, Regensburg, Germany. E-mail: [email protected]

1. Introducing a mystery called consciousness To illustrate the phenomenon of consciousness, we can use the picture of a very exclusive party, a dinner for only one, to which the other 7 billion people (and especially the scientists) are neither invited nor admitted. Moreover, everybody can throw their individual party in a nearly inaccessible space inside their head. However, scientists seem to be like curious neighbors, almost like spies. They have developed instruments and methods to spy upon what happens inside these secret rooms. The recent results of this enterprise have given rise to new rumors and hypotheses, (some of them quite unconfirmed) about the hidden procedures we are used to call “consciousness”. Their interpretations are lined up in a wide range between the still much appreciated idea of an immaterial spirit and the somehow disappointing conception that our valued “self” might be nothing but an illusion created by the arbitrary neuronal fireworks of a horde of cells. If we consider our consciousness and ourselves in an introspective way, we recognize a natural and likewise surprising state: unity and coherence. This experience prevails in spite of, and even against, our knowledge of the multitude and diversity of elements, which physically, chemically and biologically constitute our brain and form our mind (e.g., perception, memory, cognition…). Furthermore, confronted with the enormous diversity of the perceived world around us, this brain or mind seems to create order and entity in an astonishingly easy and effortless way. For centuries the explanation of this experience seemed to be obvious: There had to be a pre-existing blueprint for the architecture of the world and an observing person-like, separate instance in our mind, which constructs and reflects unity and coherence inside and outside of man – an idea that can be traced back to the considerations of Descartes, John Lockes and even Thomas of Aquin. The homunculus-model was born and the best way to understand human consciousness henceforth seemed to elucidate the nature, work and localization of this enigmatic instance. This model basically is dualistic and nowadays often criticized in general (Dennett, 1991). Even if it appears in new clothing as some kind of distributed neural network (Crick & Koch, 2003) or global workspace (Baars, 1988), the dualistic character remains. We should bear that in mind when looking at some of the impressive research linking brain functions to consciousness. On the other hand, none of the current theorists seem to really explain the qualia - the subjective qualities of experiences such as the red-

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ness of red or the painfulness of pain. How private subjective experiences can emerge from objective matter remains an unsolved question. Let us apply the party picture another time to the occurrence of consciousness within our brains: However, now we have a very exclusive party with tenth of billions of neurons sitting together, chatting and communicating quite organized in order to create one single unique experience which we call consciousness. Therefore, to understand the phenomenon of consciousness, it is not enough to ask for the firing rates of single neurons but rather to ask ensembles of neurons about their connections and coherence and their contribution to the state of the whole system. One of the first essential findings in modern neuroscience regarding the activation of single neurons and its influence on coherent brain activations was formulated by Hebb in 1949. Hebb’s rule says that neurons that fire together wire together, a dictum, which still is regarded as a basis for all learning processes and neuronal reorganization. This rule expresses the importance of connectivity, relationship and coherence of cell assemblies in order to generate a consistent conscious perception and personality. Taking a closer look at the issues of unity and coherence, on the following pages we shall present a short overview of some insights in neural mechanisms under the special perspective of coherence of neuronal cells, oscillatory neural networks and temporal patterning of neuronal activity. We shall touch upon the question of how emotion and various body processes are related to the emergence of consciousness and we shall introduce some approaches and questions that might be interesting for future exploration.

2. Consciousness and coherence within the brain 2.1. An experimental paradigm illustrating the basic questions Sometimes very simple experiences of everyday life lead to fundamental questions. Concerning the field of brain and visual awareness, ambiguous figures are a good example for an experience provoking questions on the nature of mind. The pictures of the famous painter Arcimboldo are wellknown in this context since they show faces which are composed of fruits or the black silhouette, which can be perceived as a vase or as two faces. An even more astonishing figure is the Necker cube (Figure 1). Looking at it for some time the perception switches between the two representations. They are equally possible interpretations of one real object, obviously competing in consciousness. Moreover, one may even achieve to make this change happen deliberately (Blackmore, 2005). This very simple figure provides a fine opportunity to reveal the neural correlates of a particular experience by imposing some basic questions. How

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are features of an object detected and bound together in the brain (a question which is known as binding problem)? Is there parallel unconscious information we can refer to when the representation changes? Which parts of the brain are activated and altered at the moment of the flipping information? What is the mechanism that makes this change happen? Following these and other questions, we can detect certain brain areas like e.g. V1 (the early part of visual cortex), V4 (later visual areas) and parts of the temporal cortex, which are related to the processing of visual information (Koch, 2005). But have we located consciousness in the brain this way?

Figure 1. Two ambiguous figures are the vase on a) and the Necker cube (b) in which the surface can be seen as shown in the top or the bottom shape.

Figure 2. Model of conscious perception. a) visual information is guided to the primary visual areas in the occipital cortex first before it activates further cortical areas. b) the interconnectivity of large cell assemblies in the entire cortex as illustrated by the lines within the various brain areas contribute to an internal representation of the outside world. c) the subjective conscious experience of this representation is a second level process which is schematically illustrated by the eye in the middle of the brain.


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Recent theories seem to let this expectation down. They regard consciousness as a far more complex “event”, which is not rooted in a certain brain area or in some specialized cells, but is instead based on a widespread neural network. According to their view consciousness might be understood as a function of numerous interacting brain systems and circuits. Thus, for these new approaches, theories of binding and of neural synchrony and cross-system coherence are in the focus of interest (Engel & Singer, 2001). Figure 2 illustrates this picture of consciousness evolving through the interconnectivity.

2.2. The neural correlates of consciousness The hypothesis (or “framework” as Crick and Koch prefer to describe it) of neural correlates of consciousness (NCC) was designed with two strategies. First: postponing or leaving aside the more difficult questions like the hard problem of qualia or aspects of consciousness such as emotion, memory or self-consciousness. Second: concentrating on the aspect of visual perception as an accessible and well-explored part of the brain system of primates. Their intention therefore was not to offer an overall solution, neither a theory on what conscious experience is, but to describe by what means and processes a coherent conscious experience is produced. In the following part we give an abbreviated and simplified overview focused on the role of neural cells and their interplay in neural networks. In the view of NCC hypothesis the smallest but at the same time most important “working units” are cells, the neurons. They are detecting and projecting features from the visual sensory input of the perceived objects. Connected in a neural network, firing neurons form coalitions in both, space and time. These do not only exist in certain localities of the brain, but are also interconnected across distributed brain areas. Initiated by a visual input, their neuronal activity travels as a net-wave up and down the visual hierarchy. Coalitions are sometimes in competition and only certain neuronal activity reaches consciousness. It is attention, either in the bottom-up or in the top-down mode, which in the end selects among the signals of the coalitions. The critical attribute of such a winnercoalition may probably be related to a specific way of firing, e.g., with a special synchrony, at a sustained high rate or in bursts. Moreover, synchronized firing especially in gamma range (see below) might already be of importance at the early state of forming a coalition against a rival coalition (Crick & Koch, 2003). Finally to complete the process of becoming conscious, Koch and Crick postulate the involvement of other neurons, which are influenced by the NCC. This “penumbra” provides the informational components concerning “meaning”, so that the brain can know what the firing activity of the NCC represents (Koch, 2005). To sum up, a coherent and stable representation of a visual object in consciousness seems to derive from coherence processes on a neuronal level.

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The dynamics of self-organization in fluctuating spatio-temporal neuronal activity are a characteristic quality of these processes. Especially the coordinating and coding mechanisms of these oscillatory neuronal networks have been of great interest for the scientists (see below).

2.3. Coherence within brain waves Several scientists, though with different emphases and partly in their own terms, have identified relations between binding, i.e. the theory of how a complex sensory input pattern is segregated in order to constitute a homogeneous conscious experience, and consciousness (for an overview see Engel & Singer, 2001) and thus connected the phenomenon of oscillatory activity with the emergence of conscious awareness and coherent conscious mental states in general. Oscillatory activity seems to play a significant role in different processes of the brain (see Senkowsky et al., 2008) and is usually categorized into five frequency bands, namely the delta band (1–4 Hz), theta band (4–8 Hz), alpha band (8–12 Hz), beta band (12–30 Hz), and the gamma band (above 30 Hz). An important observation in coherence within neuronal brain activity is the existence and dominance of relatively slow, rhythmic oscillations between 1 and about 12 Hz, which are predominantly active in resting states but also during memory consolidation. These oscillations represent simultaneous activations of large cell assemblies measurable through the Electroencephalogram (EEG). Especially studies on beta and gamma band activity (20–80 Hz) provided remarkable evidence (for a review see TallonBaudry, 2009) that coherent neural signals establish high functional connectivity and that therefore synchronized neural oscillation might be a potential key mechanism for the binding problem and other processing of even cross-modal sensory information in the brain. Moreover, it is gradually becoming visible, that synchronous activity in beta and gamma frequency ranges seems to be related to a multitude of cognitive functions, “such as perceptual grouping, focused attention, maintenance of contents in short term memory, poly-sensory integration, formation of associative memories and sensory motor coordination” (Uhlhaas et al., 2009). It is a special quality of the spatial distribution of neural populations to provide an enormous complexity of possible interaction, e.g., between physically separated assemblies, in the relations of power and/or phase and also across different frequency bands. So gamma band synchronization is likely to be modulated by the phase of theta rhythm, to which the disintegration and reintegration of gamma synchronized assemblies is time-locked (Doesburg et al., 2009). Recent publications (Uhlhaas et al., 2009; Cohen, 2011) emphasize the role of time as a factor, which may enable neural networks to code, process and transmit rich information. Temporal dynamics of neural activity depend


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at least partly on the oscillatory properties of electro-physical brain activity. These oscillations provide a remarkable bandwidth for the processing of information, as activity may take place independent of and parallel with other frequency bands. Thus, multiple functionally distinct networks (Cohen, 2011) can use many possibilities or dimensions of processing information and take advantage of complex spatiotemporal coding patterns (Akam & Kullmann, 2010). At the moment, a basic field of investigation is the establishment of temporal relations between the neural discharges across locally distributed cell assemblies by oscillatory rhythms. Neurons that are phase-locked to oscillations synchronize their firing. Moreover, internally generated oscillation patterns can also provide stable relations by tiny systematic or additive delays of the oscillation cycles (near zero phase lag, Uhlhaas et al., 2009). Especially these are thought to offer possibilities of temporal encoding based on phase relations. In this context, gamma rhythms of the brain raised sometimes an enthusiastic attention as a possible component of NCC. A common interest of latest research on this issue is therefore the demonstration of gamma influence on the timing of spike activity and of its affection of cortical computation (Jia & Kohn, 2011). So, e.g., Masquelier et al. (2009) suppose a role of gamma as a temporal reference frame for the encoding of stimulus strength and Fries (2009), to give another example, underlines the role of gamma band synchronization for the interaction of neuronal assemblies. He emphasizes that effective network communication might rely substantially on precise temporal relationships. A few publications (Ray & Maunsell, 2010; Burns et al., 2010) nevertheless seem to be skeptical about the role of gamma and the analysis of the temporal relationship between spikes and gamma presented until today. As a worst-case scenario rising from this criticism, Jia and Kohn (2011) claims the possibility that gamma is “simply a resonant frequency” and a mere epiphenomenon of neuronal network activity. So at the moment we have to conclude, that there are some promising approaches to components of the NCC, which in the long run might help to constitute a naturalistic theory of consciousness. Yet, there is no complete and fully convincing conception up to now. Methodical and mathematical efforts in examining especially the temporal dynamics and information coding in electrical brain activity may be necessary (Cohen, 2011) before we can reach the safe ground of not only correlative but causal findings.

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2.4. Brainwaves and dysfunctions It is a nowadays commonly acknowledged fact, that the oscillations of brain activity correlate and change with different states of mind like awareness, rest or sleep and that their frequency and amplitude display discernible and reproducible features for each of such states (for a multitude of examples and review see Tassi & Muzet, 2001). Taken the synchronization of neural activity within and across different brain regions for granted as a normal feature and functional property of cortical and subcortical networks, it is not devious to conceive, that mental dysfunctions or disorders may also be correlated with corresponding features of oscillatory brain activity. Indeed there is consistent evidence across many studies, which associates disorders like in schizophrenia and autism with altered neural synchrony not only in local brain areas, but also concerning long-range synchronization (for reviews, see Uhlhaas & Singer, 2006; Uhlhaas et al., 2009). Current medical theories of pathological brain states such as schizophrenia or autism assume a disconnection syndrome as their basic pathophysiological mechanism, so the impairment of neural synchrony observed in these diseases is consistent with their medical explanation. Moreover it is remarkable, that some of the changes in anatomical conditions and neural transmitter systems, which are characteristic for the above-mentioned disorders, are strongly related with processes involved in the synchronization of neural responses. However, whether reduced neural synchronization is a cause or a consequence of the disconnection syndrome remains still difficult to say at the moment. But all in all it completes the picture and supports the view that normal mental functions depend crucially on the exact and appropriate tuning and the well-balanced and joint practice of all players in the “swinging brain orchestra”. This probably opens a door for new forms of medical treatment: For future studies Uhlhaas et al. (2009) propose to consider the use of biofeedback signals as measures of neural synchrony, as biofeedback could provide means to alter brain states in disorders, which may be helpful for patients in the attempt of voluntarily controlling and changing aberrant activity.

3. Synchronization within various body processes 3.1. Body-emotion-mind synchrony When talking about consciousness, the brain mainly is treated as the central organ determining one’s states of consciousness. However, perceptions, feelings, emotions are provoked and dependent on the entire state of the


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innerved body. Thus, in the following we should pay attention to the correlations between body processes, emotions and consciousness. Every breath we take, every move of a hand, even every thought we form happens in a state of emotion. We feel ourselves. Emotions are, though we are sometimes not aware of them, related with our basic body functions and their interoceptive perception, with the sensory input and our often automatically run response programs, and as well with a multitude of cognitive processes like attention, memory or judgment. Surprisingly, language, as a high level property and capacity of developed consciousness, reveals an intimate connection between bodily states and emotion. So we refer to the fast pounding heart, to shivering hands or an outbreak of sweat, when we want to describe fear and derive metaphors from sensorial experiences when for example affection corresponds with warmth (“I am warming up to her”). Following the latter observation it was Lakoff and Johnson (1980) who promoted the thesis of embodied cognition, which supposes that our mind may be inherently embodied and cognition grounded in bodily experience. As a consequence we would have to understand brain and consciousness as only a part of a larger dynamic system, which comprises the wholeness of our bodily existence and experiences. A recent advance, stressing the relevance of emotions for the emergence of consciousness, comes from Damasio (1999, 2010), who sees body and emotion strongly involved in “the making of consciousness”. Damasio divides emotion into two components, a conscious experience of emotion, which he names feeling, and a behavioral and physiological one that has bioregulatory aspects (for him ‘emotion’). This emotion is a primary evolutionary mechanism, which allows an organism to keep homeostatic balance and physical coherence, for example by adjusting its inner milieu or, in response to challenging conditions of the environment, by means of approach and avoidance. In this sense, emotion can be observed already on a protozoic and cellular level. According to Damasio, human feelings are formed by emotions based on brain functions, which stem from a long evolutionary development. Individual learning and cultural influence however shape emotions in regard of their trigger and expression. The brain is perpetually receiving emotional information, which is processed in neural maps. These are then compiled in somatosensory centers. It depends on the reading of these maps of recorded emotional changes, when and how feeling occurs. The brain creates representations of these changes, which can be perceived in consciousness. This is what Damasio calls feelings. In this way, feelings can be understood as rooted in processes of homeostatic regulation, which occur most of the time quite unconscious at the level of cells and neural activity. The interoceptive information they provide might constitute what Damasio names the core self, as a grounding of the

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personal autobiographic self (which is composed during personal history) and as a basis for our experience of invariance and continuity (Tsuchiya & Adolphs, 2007). Damasio’s notions on the correlation between body and conscious emotional experiences are here in touch with another fundamental issue of emotion theory, the concept of response coherence, which can be traced back to the work of Darwin (1872). It implies a crucial role of emotions in organization and synchronization of subjective, behavioral and physiological responses, so that an individual can cope coherently and successfully with the changing environmental challenges (Levenson, 1994, 2003). The experimental findings on this theory are still inconsistent and sometimes contradictory, but a recent study (Sze et al., 2010), which is using a new within-individual approach, seems to provide at least some support for “the important role that organs controlled by the autonomic nervous system play in emotion and the critical contribution that afferent feedback from these organs plays in the construction of subjective emotional experience” (Sze et al., 2010). What are the consequences of all these considerations? If we accept that feelings have their basis in the representations of the body, a broader view of consciousness is available. Combined with the NCC framework we have now some evidence that certain types of cells and their excitability, not only in brain but also throughout the whole body, play an important role for a unified behavior, for the emergence of a coherent consciousness and for the experience of coherence and unity itself.

3.2. Synchronization between heart and brain An attempt that follows the above-sketched lines, but goes further and is more specific, is the “heart rhythm coherence hypothesis” proposed by McCraty et al. (2009). Based on findings in which positive emotions were related to physiological, emotional and cognitive synchrony, McCraty et al. postulate a core role of the heart in the synchronization of the psychophysiological network and also heart-brain synchronization. In this view the heart is the great generator of mental, emotional and physiological synchronous and harmonious interaction within and across systems. It both reflects and affects the dynamics of the body as a whole. Therefore, micro-and macro-scale temporal cardiac patterns are in strong relation to an optimal functioning of the human organism. Furthermore, McCraty et al. propose to regard the heart’s electromagnetic fields as a medium of communication from the cellular to the systemic levels, capable to convey information between individuals and to interact with energy fields in the environment. Then, if we really want to follow McCraty et al., the heart would appear to be the source of a global signal of synchrony and interaction that might help to transform and improve the life processes not only of humankind but of the


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whole world. However, we should be very careful here. While there are serious indicators for a close relationship between the heart dynamics and emotions, from a physical and physiological standpoint there is no mechanism known that would allow the detection of an individual’s heart process by another biological system apart from this individual’s body.

3.3. Synchrony between biophotons and the brain Besides the low-frequency electromagnetic body-processes measurable through the electroencephalography (EEG), electrocardiography (ECG), etc. there are processes of non-neuronal origin, e.g., those emitting light within or close to the visible spectra. Such ultra-weak photon emission (UPE), also named biophoton emission reflects human oxidative status (van Wijk et al., 2008). Measuring this radiation requires single photon detectors, e.g. photomultipliers. The measurement process itself has to take place in a dark room because the measurement of biophotons, e.g. from the hand surface, results in only a few photons per 100 milliseconds. Studies with meditators have indicated that meditation might be a way for regulating the biophoton intensity (van Wijk et al., 2005, 2006). Investigations looking for a correlation between biophotons and the brain waves have demonstrated a weak but significant phase and frequency coupling between photon emission and the alpha rhythm of the brain (van Wijk et al., 2008). A debate about biophotons came up asking whether the photon emission is just a random process during a certain condition of the body or whether it is coherent to other body processes and thereby in a causal relationship. Analyzing the distribution of biophoton emission Popp hypothesized that the photons might not be emitted independently, but rather are coupled to another process probably similarly to the laser principle (Bischof, 1995). EEG waves are deeply involved with the basic functioning of the brain but the origin and the exact function of EEGs has remained a mystery. The EEG waves show coherent changes of large cell assemblies even in distant brain areas. Therefore, the brain has to be seen as a coherent system (Rahnama et al., 2011, Salari et al., 2011a, 2011b, 2012). Neurons incessantly emit biophotons (Bókkon et al., 2010, 2011a, 2011b; Isojima et al., 1995; Kobayashi et al., 1999), and the intensity of biophotons is in direct correlation with neural activity, cerebral energy metabolism, EEG activity, cerebral blood flow and oxidative processes (Isojima et al., 1995; Kobayashi et al., 1999). The subject of biophoton emission in the brain is still in an early stage of development and needs more accurate experimental methods for proper analysis. It has, nonetheless, been demonstrated that EEG activity has a significant correlation with biophoton emission in the human brain (Dotta et al., 2012; van Wijk et al., 2008; Kobayashi et al., 1999). The claim of biophoton coherence from different biological systems has been repeated in a couple of scientific articles, but it is still not definite and requires concrete experimental proof (Salari &

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Brouder, 2011). Furthermore, it has been suggested that biophysical pictures may emerge due to redox regulated biophotons in retinotopically organized cytochrome oxidase-rich neural networks during visual perception and imagery within early visual areas (Bókkon, 2009). One recently obtained result by Sun et al. (2010) is included in the experimental evidence for the above hypothesis. It demonstrates that neurons can conduct photon signals. Moreover, Wang et al. (2011) presented the first experimental proof of the existence of spontaneous and visible light induced biophoton emission from freshly isolated rat’s whole eye, lens, vitreous humor and retina. They proposed that retinal phosphenes may originate from natural biophotons within the eyes (Bókkon, 2008; Wang et al., 2011). Unregulated free radicals and excited species can produce a transient increase of biophotons in different areas of the visual system. If this excess of bioluminescent photon emission exceeds a threshold, they can appear as phosphene lights in our mind. It appears that seeing a brilliant light in near death experiences (NDEs) may be due to bioluminescent photons simultaneously generated in the recovery phase of numerous areas of the visual system (Bokkon & Salari, 2012). Stemming from the pioneering experiments of Gurwitsch in 1920s, some researchers confirmed that cellular interactions can be mediated by electromagnetic fields e.g. biophotons (Fels, 2009). The overwhelming majority of these experiments focused on the study of electromagnetic cellular interactions examined in the optical region. A good review of the historical and recent theories and experiments on electromagnetic cellular interactions has been done by Cifra et al. (2011). As a conclusion, it may be possible that in addition to electrical and chemical signals propagating in the neurons of the brain, signal propagation may take place in the form of biophotons, too.

4. Synchronization with the outside world 4.1. Schumann-Resonance and brain waves Despite the brain functions and rhythms being generated within the brain, they are influenced through their connection to the outside world, usually and mainly through our sensory organs. One might ask the question whether there are external fields, which we do not perceive consciously but to which we are coupled in a synchronous way. The neuronal functionality is based on electro-chemical processes and the hereby-generated electromagnetic fields radiate through the scalp and can be detected through electrodes or magneto sensitive SQIUD sensors. This asks for the hypothesis whether there are external fields, which could be strong enough to influence or even synchronize our brain with the environment. Such an electromagnetic field is given by the resonance of a wave travelling around the earth between the surface and the ionosphere. It was dis-


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covered in 1952 by W.O. Schumann and therefore is called SchumannResonance (Schumann, 1952; Schuman & König, 1954). Determined by the geometry of the earth and the speed of light, several modes with frequencies of 7.8 Hz, 14.1 Hz, 20.3 Hz, and higher modes are excited predominantly through the more than 100 thunderstorms occurring each second on our planet. Nevertheless, the power density of the Schumann radiation is quite small and due to the big wavelength it requires a large coil to make them measurable. Interestingly, as the most dominant frequencies lie in the range of the most dominant brain oscillations, e.g. 7.8 Hz is in the upper theta band, several researchers have tried to look for possible interactions. Persinger in 1989 for example tried to evoke altered states of consciousness through stimulation of the temporal lobe with ultra-weak electromagnetic frequencies in the range of the Schumann resonance and he reported great success. However, as he had no double-blind setting, many scientists rather attributed the observed effects to the psychological experimental setting than to the electromagnetic stimulation. Other studies could not replicate Persingers entrainment effect and a recent replication into which the author is involved does not show a clear entrainment effect either. This is supported by the fact that in order to evoke neural discharges in the brain one requires magnetic pulses as strong as one Tesla as done by the transcranial magnetic stimulation (TMS). Therefore, the question of whether and how the Schumann resonance may have an influence on humans’ state of consciousness is still unclear and doubtful even if the frequency distributions show a nice parallel and the theta band activity can be associated with a somnolent and receptive state of consciousness.

4.2. Synchronization between Human Brains? A final question about consciousness and coherence results from a subjective experience many people report: it is a kind of extrasensory or telepathic connection between people, independently of their distance. Some take this phenomenon for granted, but unfortunately it has presented itself as a special challenge to prove its existence. Studies on so-called remote viewing have already demonstrated some evidence that such a connection through extrasensory perception might exist in specially gifted people (Persinger et al., 2002; Roll et al., 2002). In several neurophysiological experiments e.g., by Wackermann et al. (2003; 2004), Hinterberger et al. (2008), and Ambach (2008), researchers tried to demonstrate coherence between brain waves of closely related but spatially separated people. Hinterberger et al. measured EEG simultaneously in people more than 700km apart from each other. The experimental setting for investigating distant EEG correlations was realized between laboratories in Northampton (UK) and Freiburg (Germany) or Tübingen (Germany). EEG systems in the related labs were synchronized using the

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DCF time signal broadcasted from Frankfurt (Germany). As a result of these studies several experiments could show a small but significant effect in the alpha band. However, taking together all experiments there is still no consistency and the effects found remain very small compared to the normal size of a neuronal evoked brain potential. From this perspective, we are still in the process of asking ourselves the essential question of human consciousness: how connected are we really?

5. Conclusion Summarizing the results of this short introduction into synchronized processes influencing, provoking, or even determining phenomena and states of consciousness, an interesting threefold picture appears: 1) By focusing on the brain and its mechanisms, coherency might be an inherent and essential (perhaps evolutionary) property for creating conscious experiences. The detection and understanding of coherent characteristics may be helpful for the understanding of the phenomenon as a whole. 2) Expanding the search on synchrony between brain and other body functions also revealed significant relations between body functions and brain processes and its correlated states of consciousness. However, one should be aware that not all body processes show such relationships. We have demonstrated this exemplarily on the biophotons. 3) Even harder to show experimentally are the interplays of coherence between consciousness-related processes with external processes, which might be perceived through other channels than the primary sensory system. While the induction of states of consciousness through stimulation of the sensory system can be an everyday experience (e.g., through such trivial acts like listening to music, enjoying the scenery of a landscape, meeting people, etc.) the effects of extrasensory perceptions are very hard to demonstrate scientifically. This was shown exemplarily on the Schumann resonance and on telepathy research. Therefore, it is still challenging (and at least on a human level) to explore the synchronization of an inner and private process of consciousness with the outside world. Finally, one can observe a decrease in synchrony and interconnectedness of consciousness with the distance from the brain. This is a natural rule of all objects in this world. However, there might be a pinhole within the brain opened up by quantum physical entanglement phenomena. Here, one might find an interconnectedness and coherence beyond spatial limitations through non-local correlations. Then, we might have to get used to the idea that consciousness is embedded in and emerging from a much larger background or unity than we thought before. However, theories connecting consciousness and quantum physics are still subject to a hot debate (see researchers like Penrose, Hameroff, etc.) and it would be a huge step further


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if one came up with experiments that bring light into the mystery of our unknown interconnectedness with the world.

Acknowledgement TH thanks the Heiligenfeld Kliniken, Bad Kissingen (Germany) for the support in consciousness research.

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Chapter 13

Cytoskeletal electrostatic and ionic conduction effects in the cell 1

Douglas E. Friesen , Travis J.A. Craddock 1,5 and Jack A. Tuszynski

2,3,4

5

, Avner Priel

1

Department of Oncology, University of Alberta, Canada; 2Department of Medicine, University of Alberta, Canada; 3Center for Psychological Studies, Graduate School of Computer and Information Sciences & College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA; 4Clinical Systems Biology Group, Institute for Neuro-Immune Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA; 5Department of Physics, University of Alberta, Canada Abstract: The cell cytoskeleton, composed of actin filaments, microtubules, and intermediate filaments, forms an organizational basis of the cell. It provides mechanical strength, physical tracks for transport of cellular material, and a potential communications system. In addition, the electrostatic profile of the cytoskeleton theoretically supports semi-conduction, ionic wave propagation, and directed signalling via oscillating dipole moments. In particular, the microtubule subunit protein tubulin possesses a large dipole, and coupled with the significant negative charge of its C-terminal tail, offers microtubules unique electrostatic properties capable of propagating electrostatic effects throughout the cell. Experimental in vivo quantification of these effects could uncover a biophysical basis for many electromagnetic therapeutic techniques, including those impacting mitosis and, therefore, cancer. Correspondence/Reprint request: Dr. Jack Tuszynski, Department of Oncology, University of Alberta, 11560 University Ave, Edmonton, AB, T6G 1Z2, Canada. E-mail: [email protected]

1. Introduction Over several decades a body of scientific data has accumulated indicating the existence of electromagnetic properties within living organisms (Cifra et al., 2011a; McCaig et al., 2005; McCaig et al., 2009; Kirson et al., 2004; Pokorny et al., 2011; Funk et al., 2009). In general, protein and enzyme

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semiconduction was disregarded, as the electron activation energies in proteins were measured above the ~0.49 eV level usually observed in biological processes. However, most of these measurements were carried out in dried systems. Living systems function only when hydrated, typically at 80% water content. Rosenberg (1962) demonstrated that water causes significant change in protein electron activation energy which can improve protein conductivity by a factor of 1010. Hydration of biomolecules is clearly of importance, and reviewed in Tuszynski et al. (1998). Additionally, certain protein structures seem designed for high electron conductivities and can be viewed in analogy to donor and acceptor impurities in solid-state semiconductors. For example, cytochrome oxidase, a key protein in the respiratory electron transport chain of mitochondria, has a measured activation energy of ~0.3 eV (Cope & Straub, 1969), capable of acting as a solid state device. Low-dimensional macromolecules such as actin filaments, microtubules, and collagen have been implicated in supporting self-trapped localized electron or exciton states, which possibly give rise to delayed luminescence observed in biological systems (Brizhik et al., 2000; Brizhik et al., 2001; Gulino et al., 2005). However, most proteins have activation energies on the order of 1.4 eV (Bone et al., 1978). These would require additional mechanisms to support conduction. Finally, conduction within individual proteins may be different than conduction across the interfaces of macromolecular structures (Tuszynski & Kurzyński, 2003). Indeed, the latter may be what is measured when an electrode is placed across a structure comprised of many thousands of individual molecules (Tuszynski & Kurzyński, 2003). Thus, conductivity may be higher in individual molecules than previously deduced from the conductivity of molecular aggregates (Tuszynski & Kurzyński, 2003). As emphasized in the monograph by Tuszynski & Kurzyński (2003), charge transport in bio-molecular systems significantly differs from the free conductive behavior of electrons and holes in metals and semiconductors since even a single electrostatic charge in a bio-molecule measurably perturbs its environment by acting in a manner similar to a polaron in a solidstate sample. Importantly, at sufficiently low temperatures environmental disturbances accompanying charge transfer in proteins may take place with the aid of quantum tunneling effects and biomolecular electron transfer can range over fairly long distances, up to several nanometers (Tuszynski & Kurzyński, 2003). Recent high-profile publications demonstrating quantum tunneling in photosynthesis are excellent examples (Engel et al., 2007; Arndt et al., 2009; Sarovar et al., 2010). The term, organic semiconductor, has been used to describe organic compounds that exhibit properties consistent with electrical conductivity. These can be conveniently grouped into three categories: (a) molecular crystals (exhibiting van der Waals bonds), (b) charge transfer complexes (exhibiting covalent and coordinate bonding) and (c) organic polymers. Of these three types, charge transfer complexes are of

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primary biological importance because they can be found to generate free energy in the processes of respiration and photosynthesis. However, not all biological conduction can be described by charge transfer complexes since sometimes electron donors and acceptors become isolated from each other and charge carriers may persist being localized in the same molecular neighborhood before hopping to another molecular location (Tuszynski & Kurzyński, 2003).

2. Cytoskeleton’s electrostatics The cytoskeleton is a major component of all eukaryotic cells. It consists of thin rod-like filaments that span the cytoplasm. There are three major types of filaments: actin-based filaments (i.e., actin filaments (AFs)), tubulinbased filaments (i.e., microtubules (MTs)), and intermediate filaments (e.g., neurofilaments, keratin). These protein filament structures are interconnected via an assortment of other proteins and interact with motor proteins that carry molecules by stepping on protein filaments of the cytoskeleton. There are at least three well-studied mechanical functions of the cytoskeleton in vivo: (a) providing mechanical strength of the cell, (b) segregating the chromosomes and (c) participating in the transport of macromolecules via motor proteins. Finally, water of hydration, which decorates the surfaces of proteins, theoretically exhibits hexatic ordering of its dipole moments (Tuszynski et al., 2008). This indicates that ordered water may possess interesting polarization properties, which could add to an overall ferroelectric behavior of protein filaments in solution (Tuszynski et al., 2008). While ferroelectric aspects of microtubules have been modelled, this property has not been definitively proved (Tuszynski et al., 1995; Brown & Tuszynski, 1999b; Tuszynski, 2008). Nevertheless, a number of researchers speculate that the cytoskeleton is involved in signalling and information processing (Hameroff, 1987; Priel et al., 2005b; Jibu et al., 1994). Maniotis, Chen and Ingber (1997) demonstrated that MTs transduce mechanical forces over micron-long distances inside the cell and even into the nucleus suggesting an efficient signalling phenomenon that can be related to MT piezoelectric properties.

2.1. Microtubules MTs are cylindrical protein polymers of tubulin dimers found in nearly all eukaryotic cells with key roles in intracellular transport, cell organization, motion, and division. MTs are long hollow filaments made up of αβ-tubulin dimers (Dustin, 1984) and have outer diameters measuring ~25 nm and inner diameters of ~15 nm. Tubulin is a major protein of the eukaryotic cytoplasm accounting for ~3% of the protein mass (Hiller & Weber, 1978). MTs, typically composed of 13 ‘protofilament’ strands of αβ-tubulin, have ~1250 tubulin dimers per micron of length. Tubulin polymerization can be

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controlled by physical (temperature) and chemical (pH, concentration of ions) parameters to produce closely or widely spaced MTs, centrioles, sheets, rings and other structures (Dı́az et al., 1994; Hirokawa et al., 1988). Importantly, MTs have an electric and functional polarity (Tuszynski et al., 2008).

2.2. Dielectric properties of microtubules The electric properties of tubulin and MTs are of importance to a biophysical understanding of cellular processes. The dielectric properties of MTs have previously been reviewed by the authors (Tuszynski et al., 2008). In theory, MT electric properties are composed of both intrinsic protein effects and ionic effects due to condensed counter-ion clouds, which arise due to the electrostatic attraction of positive ions by the negative surface charge of MTs. This is supported by various experimental observations (Priel et al., 2006a). Aligned MTs have been observed by Vassilev et al. (1982) to assemble in vitro in the presence of electric fields with strengths of ~10 V/cm. As well, AC electric fields of frequency 100–300 kHz and strengths of ~2 V/cm have been observed to have significant effects on mitosis in cancerous cell lines over 24 hours, as reported by Kirson et al. (2004). This latter aspect has been hypothesized to be due to disruption of the MTs of the mitotic spindle (Kirson et al., 2004) and will be discussed in further detail in section 4.2. While experiment supports the theory of MT conductivity, direct measurements of biopolymer conductance are inherently difficult due to polymer structural heterogeneity and instability, the liquid state of samples, and the large range of effects of environmental factors, such as pH, temperature, and ionic concentrations on biological systems. Although these challenges remain an obstacle, experiments continue to investigate both the intrinsic (Fritzsche et al., 1999a; Fritzsche et al., 1999b; Goddard & Whittier, 2006; Umnov et al., 2006) and ionic (Umnov et al., 2006; Priel et al., 2006a) conduction properties of MTs. The Japanese team of Minoura and Muto (2006) provided such an attempt. In their experiment the conductivity and dielectric constant of MTs was evaluated using an electro-orientation method. This is a unique method as in the absence of electric fields MTs exhibit random ‘Brownian’ movements. However, in fields with AC frequencies below 10 kHz they exhibit a flow motion as a result of ionic convection. This convection effect was avoided by applying electric fields with a frequency greater than 10 kHz. With sufficient field strength, above 500 V/cm, and frequencies in the range of 10 kHz–5 MHz, MTs were successfully oriented in solution by the electric field. As a point of interest, this frequency range overlaps with the range used by Kirson et al. (2004). As an example, in a 90 kV/m field at 1 MHz MTs aligned within several seconds (Tuszynski et al., 2008). From these measurements MT ionic conductivity was estimated at


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150 mS/m. A paper by Sanabria et al. (2006) used impedance spectroscopy to measure the dielectric constant of tubulin as ε = 8.41, roughly the centre of the Minoura and Muto range, lending further support for this experiment. Experiments to measure the intrinsic conduction property of MTs can also be found in the literature. By making electrical contacts to single dry MTs on a substrate containing gold microelectrodes Fritzsche et al. (1999a) measured the intrinsic resistance of dry MTs to be much greater than the 40 MΩ/µm resistance of their wires. Additionally, they also measured MTs with a 30-nm thick nickel coating (Fritzsche et al., 1999b) and the resistance was found to be orders of magnitude lower with conduction due entirely to the metallic coating. Umnov et al. (2006) attempted direct measurement of MT conductivity. They determined a 90 S/m upper bound on the conductivity of a single MT, corresponding to a minimum 240 MΩ for a 10 µm MT (24 MΩ/µm), close to the Fritzsche et al. (1999b) result. While these experiments show progress in the measurement of the intrinsic conduction properties of biopolymers, they ignore solvent effects, which are of extreme significance to biological systems. Modelling MT conductivity has been performed with a Hubbard model with electrons hopping between tubulin monomers (Tuszynski et al., 1998; Brown, 1999). This model predicts MT resistance to be ~0.2 MΩ/µm. This is within the same order of magnitude as the Goddard and Whittier based result. However, it is two orders of magnitude smaller than dry MTs, which is to be expected. On the other hand, the Hubbard model conductivity value for MTs is two orders of magnitude smaller than that of the ionic solution surrounding the MT as used by Minoura and Muto (2006). Again, this comparison is not unexpected since ionic conductivity of aqueous solutions is a wellstudied topic while data on protein filament conductivity is only beginning to accumulate.

2.3. Tubulin electrostatics The building block of MTs, the protein tubulin, exists as a heterodimer of two closely related 55 kD monomers called α- and β-tubulin. These two related proteins are highly conserved throughout eukaryotic cells. Nogales et al. (1998; 1999) computed the structure of tubulin from an X-ray crystallographic image initially at 3.7 Å resolution. This was later refined (Löwe et al., 2001) to 3.5 Å. Tubulin consists of a β sheet core encased by surrounding α helices. It is generally considered to be composed of three main domains. The first is a Rossmann-fold nucleotide-binding domain found in the Nterminal region. The second is an intermediate domain containing a mix of four-strand beta sheets and three helices. The third domain consists of two antiparallel helices that cross the first two domains. The outer surface of the tubulin dimer possesses the C-termini, which extend outwardly from the MT surface and carry a significant amount of negative electric charge (as

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much as 40% of the total monomer charge can be attributed to a C-terminus), as shown in Figure 1. Since the MT surface charge is also negative, Coulomb repulsion causes an extended C-termini conformation. The ends of an MT possess a different amount of net charge as was demonstrated through massive molecular dynamics computations (Baker et al., 2001). It can therefore be concluded that the MT cylinder supports an intrinsic electric field E as predicted earlier (Sataric et al., 1993).

Figure 1. Three scales of MT constituent protein tubulin (with C-termini extended) including electrostatic maps. Light gray – α-tubulin, Dark gray – β-tubulin. (A) Top – Single tubulin dimer, showing C-termini that extend outwardly from the protein surface. Potential phosphorylation sites are highlighted: Blue - Threonine, Cyan – Serine. Bottom – Electrostatic map of the tubulin dimer, showing negative charges on each C-terminus, which are responsible for its extended conformational state since the surface charge is also negative leading to Coulomb repulsion: Blue +0.5 kT/e, Red -20.5 kT/e. (B) Cylindrical B-lattice MT showing C-termini extending from the MT. (C) Top – 9 dimer B-lattice patch. Bottom – Electrostatic map. (D) Top – 7 dimer Alattice patch. Bottom – Electrostatic map. Scale bars 5 nm. Reproduced from (Craddock et al., 2012) under the Creative Commons Attribution License.

In proteins, virtually every peptide group in an α-helix possesses a considerable dipole moment on the order of p0 = 1.2 × 10-29 Cm = 3.5 debye (Tuszynski et al., 2008). These dipoles are almost all parallel to the helix axis which gives rise to an overall dipole moment of this particular helix. It is generally accepted that this large dipole moment of an α-helix has an important biological role (Hol, 1985). Since the tubulin dimer contains several α-helices which are not oriented randomly it is not surprising that each tubulin dimer possesses a large net dipole moment p (Tuszynski et al., 2008).


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On the other hand, closed loops lend themselves to the cancellation of an overall dipole moment (Sataric & Tuszynski, 2005). The values of the dipole moments and net charges have been computed (Tuszynski et al., 2004a) using the sequences of the various homologous isotypes of tubulin biochemically characterized in the literature (Lu et al., 1999). This was done by creating probable structures for human tubulin isotypes listed in the SwissProt (Boeckmann et al., 2003) database using the computer program MOLMOL (Koradi et al., 1996) with the Nogales structural data. Table 1 summarizes the results for the key variants of tubulin found in human cells. A sample of several isotypes of human tubulin was examined (α = A, β = B, γ = G, δ = D, ε = E) and the results of our calculations of their dipole moments and electric charges are shown in Table 1 following Tuszynski et al. (2004a). The values listed include the presence of C-termini. Name TBA1 HUMAN TBA2 HUMAN TBA4 HUMAN TBA6 HUMAN TBA8 HUMAN TBB1 HUMAN TBB2 HUMAN TBB4 HUMAN TBB5 HUMAN TBBQ HUMAN TBBX HUMAN TBG1 HUMAN TBG2 HUMAN TBD HUMAN TBE HUMAN

Mx -467.42 -78.89 -45.56 -150.85 -256.45 -495.27 -182.81 -1146.80 -871.20 -276.97 -1066.53 715.79 545.83 -252.25 530.79

My -810.41 -1265.03 -736.51 -811.98 -1128.55 -1363.29 -1527.65 -824.54 -1117.40 -767.66 -1198.28 -1581.83 -1569.35 -1285.41 -498.82

Mz 1106.86 791.52 1289.43 911.68 647.11 2041.15 1724.69 2055.20 1953.30 586.22 2496.65 -602.89 -345.79 367.19 446.95

M 1449.27 1494.33 1485.65 1230.14 1325.95 2504.03 2311.21 2493.77 2413.08 1004.82 2967.60 1837.94 1697.16 1360.42 854.59

Net Charge -24 -23 -24 -23 -24 -25 -25 -25 -24 -18 -24 -10 -13 -5 -6

Table 1. Dipole moments (in debye) and electric charges of isotypes of human tubulin. The x-direction corresponds to the MT protofilament axis, the y-direction is oriented radially toward the MT centre, and the z-axis is tangential to the MT surface. A simplistic estimate of the tubulin dipole moment p based on a mobile charge of 18 electrons multiplied by a separation distance of 4 nm yields a magnitude of p = 4 × 10-27 C m (1200 Debye) (Mershin et al., 2006). At physiological pH (= 7.2) MTs are negatively charged (Stebbings & Hunt, 1982; Sackett, 1995; Melki et al., 1989) largely due to the presence of a 15-residue carboxyl terminus ‘tail’ that accounts for up to 40% of the protein’s net charge. This latter feature has not been included in the electron crystallography data of Nogales et al. (1998; 1999) so previous values concerning the dipole moment by Mershin et

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al. (2004) were calculated ignoring the effect of the C-terminus. It is also known that at the isoelectric point of pH = 5.6 MTs become effectively neutral. In order to experimentally determine the dipole moment of tubulin Mershin et al. (2004) performed a surface plasmon resonance study for tubulin dimers in solution. From the preliminary data, an order of magnitude calculation for the dipole moment of tubulin resulted in |p|~ 103 debye using the 7.1 mg/mL concentration data and assuming no interaction of buffer and tubulin dipole moments (Mershin et al., 2006). This is consistent with the estimates of tubulin dipole moments listed in Table 1.

2.4. Electric fields and ferroelectricity of microtubules Experimental efforts were made aimed at measuring the electric field around MTs (Pokorny et al., 1998; Jelinek et al., 1999; Pokorny, 1999) indicating that MTs could be ferroelectric. Stracke et al. (2002) subjected MTs to moderate electric fields. Electric fields were applied to suspended MTs and to MTs gliding across a kinesin-coated glass surface. In suspension, MTs without MT-associated proteins (MAPs) moved at pH 6.8 from the negative electrode to the positive one indicating a negative net charge. An electrophoretic mobility of about 2.6 × 10-4 cm2/Vs was determined. Dombeck et al. (2003) used uniform polarity MT assemblies imaged in native brain tissue by second-harmonic generation microscopy which only worked for uniform polarity bundles, but not for anti-parallel arrangements indicating a nonlinear polarization effect. As mentioned above, Vassilev et al. (1982) observed alignment of MTs in parallel arrays due to the application of both electric and magnetic fields. This was later supported by experiments of the Unger group with electric fields in the range of 300 V/m and with magnetic fields ranging from several tesla up to 30 T as shown by Bras et al. (1998). The idea that MTs are ferroelectric was proposed over 40 years ago (Athenstaedt, 1974) on the basis of their piezoelectric properties, and more recently argued by Mavromatos et al. (1999). It is apparent that due to the strong curvature of an MT cylinder, the inner parts of the tubulin dimer structure are compressed in order to fit into a MT while the outer ones are stretched by a substantial amount of tension (Sataric & Tuszynski, 2005). This additional redistribution of excess negative charge enhances the transverse component of the net dipole moment of every dimer comprising an MT (Sataric & Tuszynski, 2005). This effect is analogous to that observed for the experimentally verified cylindrical hair cell properties (Weitzel et al., 2003). This has also been corroborated by a detailed map of the electric charge distribution for the tubulin dimer calculated in Tuszynski et al. (2004a). The ferroelectric, conductive and related vibrational properties of MT have been thoroughly investigated theoretically and computationally


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(Tuszynski, 2008). Models of coherent lattice excitations showed energy propagation via solitary waves originating from guanosine triphosphate (GTP) hydrolysis (Trpisová & Tuszynski, 1997). The model Hamiltonian contained effective dipole-dipole terms, the kinetic energy of the displaced dimers, the elastic restoring energy and the potential energy due to the electrostatic and elastic energy terms and their interactions (Tuszynski, 2008). External electric fields of the order of magnitude consistent with the axonal action potential were shown to be capable of reordering these local dipoles (Tuszynski, 2008). Energy loss-free transport along MTs has been shown possible (Trpisová & Tuszynski, 1997; Sataric et al., 1993; Sataric & Tuszynski, 2003) as a result of “kink like excitations” or solitons as an energytransfer mechanism in MTs (Tuszynski, 2008). Several models exist, but they all depend on the dipole moment of tubulin and its ability to flip. These “flip waves” can be coupled to conformational states of tubulin. Depending on the model and the parameters assumed, the speed of such waves has been estimated to be 102±1 m/s (Trpisová & Tuszynski, 1997). Electric fields generated by MTs have been modeled extensively recently by Cifra et al. (2010; 2011b) although experimental quantification of these fields remains a technical challenge in a cellular environment.

2.5. Actin filaments Actin is the most abundant protein in the cytoplasm of mammalian cells, accounting for 10–20% of the total cytoplasmic protein. Actin exists either as a globular monomer (i.e., G-actin) or as a filament (i.e., F-actin). Actin-based filaments are the simplest protein filaments, with a diameter of approximately 4 nm and variable lengths. The actin strand is a left-handed helix of actin monomers. In the 1960s Kobayashi et al. (1964) demonstrated the existence of a dipole moment of actin that is oriented roughly perpendicular to the filament axis in F-actin.

3. Electrodynamics Signalling by the Cytoskeleton 3.1. Protein filament network Cytoskeletal biopolymers, such as MTs and AFs, comprise a suitable architecture for wave propagation, which would allow communication and modulation of membrane components (Priel et al., 2005a). Recently clear functional interactions between these structures have come to light. MTs interact with AFs in neuronal filopodia guiding MT growth in neurite initiation (Dehmelt & Halpain, 2004). Indeed, several neuronal proteins bind both MTs and F-actin, thus mediating signalling between filament types. Potentially, this can orchestrate invasion of MTs into neuronal growth areas. For example, in vitro microtubule associated protein 1B (MAP1B) and microtu-

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bule associated protein 2 (MAP2) interact with actin (Togel et al., 1998), and cross-linking via both MAP2 and/or MAP1B is likely to contribute to guidance of MTs along actin filament bundles (Priel et al., 2005a). A direct regulatory interaction between AFs and ion channels has been confirmed experimentally (Cantiello et al., 1991; Cantiello, 1997). Thus, a clear and direct connection exists between the cytoskeleton and cellular membrane components. Each individual cytoskeletal component is capable of supporting ionic wave propagation. A molecular dynamics model of the dendritic MT network (MTN) where arrays of MTs are interconnected by MAP2s has been developed (Priel et al., 2005a). In this model, ionic waves propagate along the MTN and interact with C-termini of MTs to generate collective electrodynamic modes of behavior. A biophysical model of nonlinear ionic wave propagation along AFs (Tuszynski et al., 2004b) and MTs (Priel & Tuszynski, 2008) (see Figure 2) was supported by experimental evidence (Lin & Cantiello, 1993; Priel et al., 2006a).

Figure 2. Cartoon image of ion flow along a microtubule in the presence of a potential difference. Positive ions (blue) condense around the MT due to the negative surface charge (red) of the MT.

3.2. C-termini dynamics It is theorized that the C-termini of neighbouring tubulins have biophysical properties that have a significant influence on the transport of material in cells. Using molecular dynamics modelling, conformation states of the Ctermini protruding from the outer surfaces of MTs were calculated (Priel et al., 2005b) and hypothesized to strongly interact with other proteins, such


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as MAP2 and kinesin (Sackett, 1995). Each tubulin dimer is decorated with two C-termini that may either extend outwardly from the surface of the protofilament or bind to it in one of a few possible configurations (see Figure 1). The most dynamic structural elements of the MT system (i.e., its elastic and electric degrees of freedom) are envisaged as conformational states of the C-termini (Priel et al., 2005a). Each state of the unbound C-terminus evolves so as to minimize the overall interaction energy of the system (Priel et al., 2005a). The negatively charged C-termini interact with (a) the dimer’s surface, (b) neighbouring C-termini and (c) adjacent MAPs (Priel et al., 2005a). While the surface of the dimer is negatively charged overall, it has positive charge regions that attract the C-termini causing them to bend and bind in a ‘downward’ state (Priel et al., 2005a). The energy difference between the two major metastable states is on the order of only a few kBT (Priel et al., 2005b). Simulations demonstrate that ionic waves can trigger C-termini to change from upright to downward conformations (Priel et al., 2005a). This model considers MAP2 to function as an ionic “wave-guide”, transferring a conformational change in a C-terminus state to an adjacent MT. Perturbations applied to condensed counterions at the end of MAP2 force them from equilibrium initiating a travelling wave (Priel et al., 2005b). This wave is predicted to travel as a “kink” solitary wave with a phase velocity of vph ≈ 2 nm · ps-1 (Priel et al., 2005b).

3.3. Actin filaments support non-linear ionic waves AFs are linear polymers with unique charge distributions that yield an electric field that varies along their length. Originally postulated by Oosawa (1971; Priel et al., 2005a), this implies significant variations in ion densities at the polymer surface and a large dielectric discontinuity in the ionic distribution (Anderson & Record, 1990). Experiments have demonstrated the potential for ionic wave propagation along AFs (Lin & Cantiello, 1993), and models provide a framework for the analysis of these waves (Priel et al., 2005a). F-actin, the AF constituent protein, contains a fraction of its surrounding counterions in the form of a condensed cloud, which may be isolated against significant ionic perturbations in the encompassing saline solution (Manning, 1978). Due to this counterion cloud AFs may be considered non-linear inhomogeneous transmission lines propagating non-linear dispersive solitary waves, effectively acting as biological “electrical wires” (Lin & Cantiello, 1993). Condensation of the counterion cloud segregates the filament core from the ions in bulk solution, thus acting as a dielectric medium between the regions (Tuszynski et al., 2004b). This arrangement lends resistive, capacitive and inductive components to each monomer of the AF, and allows ion flow distances of a Bjerrum length from the filament surface (Tuszynski et al., 2004b).

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In solution, the positively charged end of actin polymerizes faster than the negative end (Sept et al., 1999). Actins organize head-to-head forming actin dimers. This results in an alternating distribution of electric dipole moments along the filament length (Kobayashi et al., 1964). Observed solitary wave patterns in electrically-stimulated single AFs (Lin & Cantiello, 1993) are remarkably similar to waveforms in transmission lines (Lonngren, 1978). The observation of soliton-like ionic waves is consistent with the idea of AFs functioning as biological transmission lines (Tuszynski et al., 2004b). A mechanism is envisioned where direct regulation of ion channels by AFs and associated cytoskeletal structures controls and modifies the electrical response of the cell (Priel et al., 2005a). In this picture, MTs arranged in networks receive signals in the form of electric perturbations via AFs connected to MTs by MAP2 (Rodriguez et al., 2003), or via direct MT connections to proteins by molecules such as CRIPT (Passafaro et al., 1999). The MTN may be viewed as a high-dimensional dynamic system where the main degrees of freedom are related to the conformational state of the C-termini (Priel et al., 2005b). The input signals perturb the current state of the system that continues to evolve (Priel et al., 2006b). The idea that a non-specific high-dimensional dynamical system may serve as a reservoir of trajectories in the context of liquid state machines (LSM) was proposed as an explanation for the existence of microcircuits in the brain (Maass et al., 2002). The basic structure of an LSM is an excitable medium (hence “liquid”) and an output function that maps the current liquid state. The liquid must be sufficiently complex and dynamic to guarantee universal computational power and to ensure that different input vectors will lead to separate trajectories (Priel et al., 2005a). The attractiveness of the concept that the cytosol, with its cytoskeletal structures, may behave as an LSM is clear as it provides a means for realtime computation without the need for stable attractors (Priel et al., 2005a). Moreover, the output is relatively insensitive to small variations in either the MTN or the input vectors (Priel et al., 2005a). As well, the MTN state evolves continuously even without external inputs. However, recent perturbations have a long-term effect on the MTN trajectories, i.e. there is a memory effect inherent to this system (Priel et al., 2006b). The output from the MTN may be linear regression functions that converge at or near ion channels to regulate their behavior (Priel et al., 2006b). The issue of adapting the readout requires a feedback mechanism that will, at least locally, enable change in the output function (Priel et al., 2006b). One possibility for this is a Hebbian based response where more frequent activity of certain sub-domains of the MTN output states gives rise to higher/lower density of AFs connecting to corresponding channels (Priel et al., 2005a). As mentioned above, these cytoskeletal filaments are affected by, and regulate ion channel activity. This can provide an integrated view of these phenomena in


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a bottom-up scheme that demonstrates how ionic wave propagation along cytoskeletal structures may impact channel functions, further discussed in Woolf et al. (2009).

4. Electrical effects in cells 4.1. Electromagnetic generation and sensitivity of cells The study of the effects of electrical fields on cells goes back to 1892 when Wilhelm Roux subjected animal eggs to electric fields and observed a pronounced stratification of the cytoplasm as a result (Jaffe & Nuccitelli, 1977). In the decades that followed, a number of effects have been observed which implicate electric fields and/or currents in the cytoskeletal or cytoplasmic self-organization processes (Tuszynski & Kurzyński, 2003). For example, growing tissues and organs have been shown to be sensitive to magnetic fields and to electric currents (Becker & Selden, 1985). These cell sensitivities to electric and magnetic fields has been extensively reviewed by Funk et al. (2009), McCaig et al. (2005), and Cifra et al. (2011a). In terms of an endogenous electric field generated by the cell, precise information is not yet available. The basis of cells generating electric fields through MTs and mitochondria and possibly other systems, and measurement of such fields has also been reviewed by Cifra et al. (2011a). Jelinek et al. (1999) found evidence that the electromagnetic activity of yeast cells peaked during mitosis, confirming Pohl’s earlier indirect experiments that suggested yeast cells in M phase emit the strongest electric fields (Pohl, 1980; Pohl et al., 1981). Electric field strengths have typically only been able to be measured inside membranes with voltage dye and patch-clamp techniques, leaving the strength of electric fields within the cytoplasm hitherto unknown. Initial attempts using nanosensors embedded in the cytoplasm of cells to measure the endogenous electric field in cells have shown large field strengths (~107 V/m) which were dissipated upon the addition of a mitochondrial uncoupler (Tyner et al., 2007), although such large field strengths have been recently criticized for being too large to be general to the cytoplasmic volume. Thus, further work is in progress to measure and determine the implications of endogenous electric fields (Gatenby & Frieden, 2010). This technology has the potential to create 3D electric field profiles of the cell, which would allow the study of the change of electric field profiles that occur specifically during mitosis and as a result of various cytoskeletal and metabolic profiles.

4.2. Electrostatic effects during mitosis Two aspects relevant to electrostatic effects occurring during mitosis relate to external electric fields and possible endogenous electric fields. Many find-

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ings indicate that electromagnetic (EM) fields may directly modulate the regulation of cellular growth and differentiation, including the growth of tumors (Berg et al., 2010; Funk et al., 2009). Both static magnetic and electric fields can alter the mitotic index and cell cycle progression of a number of cell types in various species (Funk et al., 2009; Tuszynski & Kurzyński, 2003). Gagliardi (2002) has done modeling work to explain all stages of mitosis as controlled by electric fields acting on charges. The impact of external electric fields on a cell theoretically depends on the cell’s shape. Calculations of the electric field strength in a spherical cell indicate that, assuming the conductivities of the extracellular and intracellular fluids of the cell are the same, due to the small conductivity of the membrane versus these fluids, the electric field strength inside a typical cell is approximately 5 orders of magnitude less than that outside the cell (Hobbie & Roth, 2007). Thus, given an applied electric field in air of 300 V/m, a cell with diameter 10 microns would theoretically experience a field of 5.4 × 10-10 V/m (Hobbie & Roth, 2007), thereby making the intracellular space shielded to a large degree from extracellular electric fields. King and Wu’s (1998) theoretical calculations for a spherical cell agree that the electric field in the protoplasm of the cell is negligibly small compared with the saline tissue around the cell, again approximately 5 orders of magnitude smaller. A more interesting picture emerges when the cell is not spherical, as becomes the case increasingly when cells enter mitosis. King and Wu (1998) have calculated the shielding occurring in elongated cells such as those found in muscle and long nerve cells by calculating the theoretical electric field inside a cell modelled as a cylinder. They found that in sufficiently elongated cells, no shielding occurs (King & Wu, 1998). Extending their analysis, they found that in bundles of elongated cells such as those in muscle, still no shielding occurs (King & Wu, 2000). Brown and Tuszynski (1999a) note that the DNA contents in the nucleus will still be protected from external fields due to being enclosed in the spherical nuclear membrane.


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Figure 3. Cytoskeletal electrostatic and ionic conduction effects in healthy and diseased states. In a healthy organism, the cystoskeleton is envisioned as operating correctly in transmitting information, especially between the cell membrane and the nucleus. External EM Fields, altered pH, or altered temperature may affect polymerization of MTs, leading to altered cell dynamics and a diseased state.

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However, during mitosis, when cell elongation typically occurs, external electric field effects may be much more relevant to cellular processes. Kirson et al. (2004) have demonstrated this in their study of applying alternating electric fields for cancer therapeutics. In 2004, Kirson et al. (2004) discovered that low-intensity, intermediate frequency (100–300 kHz), alternating electric fields had a profoundly inhibitory effect on the growth rate of various human and rodent tumor cell lines. The effects included both arrest of cell proliferation due to interference of proper formation of the mitotic spindle and destruction of cells undergoing division. This work has been shown to be effective in increasing survival rates as an adjuvant treatment to chemotherapy in glioblastoma multiforme (Kirson et al., 2009a; Kirson et al., 2009b), and has subsequently been approved by the United States Food and Drug Administration (FDA). There are two main mechanisms theorized to be at work. The alternating fields are postulated to interfere with polymerizing and depolymerizing electrostatics of MTs, due to the force of the alternating fields acting on the tubulin dimers that make up the MTs. The tubulin dimers’ dipole moment (Tuszynski et al., 1995; Gagliardi, 2002) could be affected by these external fields, especially in mitoses due to decreased shielding as explained by King and Wu. Theoretical calculations done by Gagliardi (2002) in explaining how electrostatic forces generated by MTs are at work during mitosis and influence chromosomal motion, suggest other effects could be at work when alternating fields are applied to cells in mitosis. Kirson et al. (2004) also note that during mitosis, instead of the electric field in the cell being uniform, the field gets focused to the cleavage furrow. This may have the effect of attracting any dipoles toward the furrow, and causing cell membrane rupture and post-mitotic apoptotic cell death (Kirson et al., 2004; Kirson et al., 2007). Interestingly, Kirson et al. (2009b) postulate that dividing cells of the hematopoietic system are not affected by these alternating fields due to muscles surrounding the bone marrow serving as a shield to the external electric field effects. It is known that in the presence of an external electric field, the field aligns cell division, making a high proportion of cells having their cleavage plain orthogonal to the electric field (Zhao et al., 1999). Put together, there is reasonable evidence that especially during mitosis, electric field effects are relevant to cell functioning, especially in the creation of the mitotic spindle through interactions with MTs.

5. Conclusions This chapter discussed electrostatic and electrodynamic effects either postulated or measured at the level of individual cells and below, especially those created and propagated by the cytoskeleton of the cell. A connection of these effects to the tissue and organ or even organism level is interesting but not


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simple to make (see Figure 3). If demonstrated clearly and reproducibly, this will have enormous repercussions on the field of medicine. Theories pertaining to these effects on inter-cellular communication and organism-wide communication systems are elaborated upon by Oschman (2003) and Ho (1998). Given the renewed interest in electromagnetic effects on cells and improved technology to measure such effects being developed, tremendous potential exists for discoveries of the biophysical role of electromagnetism to be quickly translated to effective therapies. It is hoped that precise quantification of the electric profile and conductivities of cytoskeletal components to test the extensive theoretical modelling of the electrostatic profile of the cell and the communication pathways created by the cytoskeleton will lead to improved therapeutics in medicine and especially new treatments of conditions where dysfunction in these pathways may be a hitherto unknown aspect of the pathology of the disease.

Acknowledgement Douglas Friesen gratefully acknowledges funding from Alberta Innovates Health Solutions and the Alberta Cancer Foundation. Jack Tuszynski acknowledges funding from NSERC (Canada).

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Chapter 14 Morphogenetic fields: History and relations to other concepts Lev V. Beloussov Laboratory of Developmental Biophysics, Faculty of Biology, Lomonosov Moscow State University Abstract. The notion of field was introduced in biology by Alexander Gurwitsch 100 years ago. Since then the “field approach” passed a tortuous way, met a strong opposition and has been used in different meanings. We review its history and discuss the relations between this approach and the more modern theory of self-organization, as well as the possible physical foundations of the field notion, as applied to biology. Correspondence/Reprint request: Dr. Lev V. Beloussov, Laboratory of Developmental Biophysics, Faculty of Biology, Lomonosov Moscow State University. E-mail: [email protected]

1. Gurwitsch’s field constructions The notion of a “field” (under the term Kraftfeld, a field of force (or forces) was introduced into biology by Alexander Gurwitsch (1874-1954) (AG) exactly one hundred years ago (Gurwitsch, 1912). From the very beginning and until now it has been most closely linked with a fundamental problem of morphogenesis, that is, the formation of new space-temporal structures during the development of organisms. Although a real physico-chemical nature of the forces involved in structural formation became elucidated step-by-step only in our days, it was obvious already at the time of first AG publications that such forces, whatever be their origin, are highly ordered both in space and time. Moreover, it had already been discovered that embryonic development obeys quite peculiar holistic laws, looking incompatible with the principles of inorganic sciences of those times. This discovery was made in 1891 by the German embryologist Hans Driesch (Driesch, 1891; see also Mocek, 1974) who demonstrated that at the early developmental stages a

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well-structured whole organism can develop from just a part of embryonic material. Driesch’s results undermined so called preformism, a wide spread believing that a complicated structure of an adult’s body correlates in one-to-one manner to a similarly complicated structure of an egg. A failure of preformism meant that a “whole” possesses the formative capacities irreducible to any more elementary mechanisms. By introducing the notion of a “Kraftfeld”, AG tried to pave a way for a rational solution of this mystery. Meanwhile, the title of his above mentioned paper from 1912, “Vererbung als Verwircklichungsvorgang” (engl.: Heredity as a process of realization) reflected the author’s reaction to the first steps of a newly born science, genetics. Welcoming its quantitative approaches, AG was at the same time disappointed by its focusing onto the problem of transmission of hereditary factors, neglecting at the same time the very process of realization of genetic information during development. This marked the beginning of a prolonged opposition of the field approach and that of a corpuscular genetics – an opposition which only recently became to be transformed into a more tolerant and even fruitful cooperation. The first field constructions were associated with the notion of a socalled “dynamically preformed morpha” (DPM), with the following description. By analyzing the morphogenetic movements of large cell collectives during formation of the different organs AG concluded that within a substantial period of development the cells are moving and reoriented as if being attracted by some unknown force, whose location coincided with the final shape of a given rudiment, not yet achieved at the given moment of development. This “prospective” shape was called DPM because before the end of development it existed only dynamically, rather than materially (like an equipotential surface of a physical field). Accordingly, the “field of forces” was defined as a territory onto which DPM extended its action. Most important, it was suggested that the field action should be described by a simple mathematic law (to be established experimentally), retaining its invariability within a large enough time period. Thus, the DPM concept was directed towards endowing Driesch’s holistic factor by measurable dynamic properties. In his next work on embryonic brain formation (Gurwitsch, 1914) firstly described a remarkable property of the so-called “prognostic” cell orientation: the radial cell axes of embryonic epithelia were oriented perpendicularly to as yet non-existing final shape of epithelia (that is, to DPM) which by the author’s idea had a capacity to rotate and reorient cell axes (Fig. 1A, B). The same concept was employed somewhat later for interpreting shape formation of some plant and fungi rudiments (Gurwitsch, 1922). Now the term “field” was for the first time included into the paper’s title, which stimulated a rapid burst of popularity of the entire concept. However, AG

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himself soon put the DPM concept aside for two main reasons. First, he was unsatisfied by its poor capacities for generalization: the DPM concept should be taken ad hoc for each next object and period of development. Second, it was inapplicable to molecular level events, which AG met with increasing interest. Meanwhile, in the context of contemporary science, the DPM concept still keeps some interesting properties. First, from the viewpoint of a modern systems theory it should be regarded as a first model belonging to the category of so-called target-oriented models (Teufel, 2011). More concretely, it can be precisely reformulated in the terms of the fields of mechanical stresses which seem to play a primary role in morphogenesis (Beloussov, 1998, 2008b). More specifically, DPM can be identified with a surface of minimal mechanical energy to which a mechanically stressed cell layer tends to approach gradually in the course of normal morphogenesis and rapidly jumps when the stresses are experimentally relaxed (Fig. 1C).

Figure 1. A concept of DPM (A, B) and its modern interpretation (C). A: DPM coincides with as yet non-achieved final rudiment’s contour MM, which has a capacity to set up the cells axes at the preceded developmental stages (contours I – I, II – II, III – III) along the bisectors between the directions normal to momentary layer’s surfaces (aN) and those perpendicular to DPM (am). B: cross-section of a part of an early neurula stage fish (Selachia) embryo. Solid curved line is DPM and straight lines the axes of cell nuclei which are perpendicular to DPM. C: Same stage cell layer of a frog embryo jumps towards the DPM configuration immediately after its detachment from the underlain tissues (arrow) indicating that the DPM position corresponds to the maximally relaxed state of the beforehand mechanically stressed embryonic tissue (A, B from Gurwitsch, 1914; C from Beloussov, 2008a).

Anyway, in early 1940’s (just during dramatic events of World War II: see Beloussov, 2008a) AG extensively reformulated his field concept by suggesting that a field of a whole is summed up from the “elementary cell fields” (Gurwitsch, 1944; posthumous publication: Gurwitsch, 1991). The main principles of the new version were the following:

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“The field acts on molecules. It creates and supports in living systems a specific molecular orderliness. This means […] any spatial arrangement of the molecules which cannot be derived from their chemical structures, or from equilibrium states such as chemical bonds, van der Vaals forces, etc. Consequently, molecular orderliness generally is a non-equilibrium phenomenon […]. The field is anisotropic […] continuous and successive […]. During cell division the cell field divides as well […]. A cell creates a field around itself; that is to say, the field extends outside the cell into extracellular space […]. Therefore, at any point of within a group of cells there exists a single field being constituted of all the individual cell fields […]. Hence, the properties of this common field will depend, besides other factors, also on the configuration of the multicellular whole. Rather than postulating independently existing supracellular fields, we now attribute their function to a field representing the vectorial addition of the individual cell fields […]. A field is somehow associated with the molecules of chromatin, but only while they are chemically active […]. A postulated field continuity may be understood molecularly in the following way: if in the vicinity of chromatin molecule A, which is at the given moment a field “carrier”, an active chromatin molecule B is synthesized, the field of molecule A induces the field of molecule B losing at the same time its own field […]. The field employs the energy released during exothermic chemical reactions in living systems to endow molecules (proteins, peptides, etc.) with ordered, directed movement… A point source of a cell field coincides with the center of the nucleus; hence, the field is, in general, a radial one… The direction of the field vectors is centrifugal (i.e. the vectors are directed from a field center to the periphery)” (excerpted from Gurwitsch, 1944). As seen from this excerpt, in spite of extensive reformulation of the entire concept, the new version of the field concept remained to be a system of directed forces, although now deriving the required energy from the local metabolic processes, rather than from the field sources which may be external to the affected staff. This should be estimated as a remarkable preview of the notion of an “active medium” employed in the modern selforganization theory (SOT) (see, e.g. Krinsky and Zhabotinsky, 1981). Meanwhile in Russia, soon after AG’s publication (Gurwitsch, 1944), a small team of DPM admirers was very much disappointed by the new field concept; in their opinion, it destroyed the very idea of an irreducible “whole”. Gurwitsch (1947) rejected these reproaches by arguing that the central role in his new concept was played by a holistic geometry of cell layers, nonseparable to its elementary components. The main advantage of the new version was a possibility to derive each new and more complicated embryonic shape from a preceding, less complex one. This was the first attempt to


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formulate what is called today the generative laws of shape formation (Goodwin, 1994). The first applications of the 1944 theory to such a task were promising (Fig. 2). It took some time to recognize that the success of the “form out of a form” idea did not verify just this field construction; the observed shapes successions could be explained in another way, better correlated with empirical data (Beloussov and Grabovsky, 2003). Today we have to conclude that in spite of several insights, this second field concept from 1944 is incompatible with subsequently discovered mechanisms of cell movements and interactions. For example, according to the 1944 concept only the repulsive cells interactions are implied, while during most important morphogenetic movements (so called latero-medial cell convergence (Shih and Keller, 1992) large amounts of cells are moving towards each other. Moreover, with the exception of few cases of negative chemotaxis (Gilbert, 2010) no distant cell-cell interactions passing via cell free space have been ever observed. (It is worth mentioning in this respect, that the explanatory principles of the routine chemotaxis models and the field models are quite different: while the first refer to quite local (point-like) factors of embryonic cells activities, the second ones are dealing with collective (as a rule, delocalized) factors. From a more general point of view, the local factors can be considered as a particular (degenerated) case of delocalized ones, but not vice versa).

Figure 2. Deriving “form out of form” on the basis of the 1944 year version of Gurwitsch’s field theory. A: three brain vesicles shape (the outer contour) is derived from a preceded one (the inner contour). (From Gurwitsch, 1944). B, B1: a similar construction for the morphogenesis of a hydroid polyp Obelia. B: its final shape (outer contour) derived from less differentiated one (inner contour) under assumption of the field interactions between the neighboring cells only. B1: same construction adjusted by adding long-range interactions (dashed lines) (from Beloussov, 1968).

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In any case, we have to accept that the main advantage of the 1944 field concept was in formulating (rather than solving) a challenged task of deriving a macroscopic type of behavior with systems-level properties from the rules describing the behavior of the systems subunits (see Levin, 2012). As learned by the history of science, to formulate a new task is much more important than to suggest its particular solution.

2. A field concept as viewed in modern developmental biology As briefly mentioned before, the field theory was rather popular in developmental biology of the third decade of the 20th century. Besides the influence of Gurwitsch’s papers, this was caused by several outstanding discoveries which may be adequately illustrated by the following experiments (Harrison, 1918). A rudiment of a limb in Urodela embryos, well before it takes a visible morphology, can produce a normal single limb: 1.

after its transplantation to abnormal location;

2.

after its fusion with another similar rudiment;

3.

out of its small part;

4.

after the mutual replacement of its constituent parts.

Besides, by inverting the limb rudiment at the different developmental stages in relation to antero-posterior (AP) and/or dorso-ventral (DV) embryo polarity it was established that at the earliest stage neither AP, nor DV limb axes has been firmly fixed: after rotations both of them have been adjusted according to the host polarity. Somewhat later the AP, and not DV limb axis became firmly fixed, the latter one becoming fixed even later. These results are directly related to the field concept because they show that the process of determination is essentially holistic: a period of development can be outlined when none of the minor rudiment elements (single cells, or small cell groups) as yet selected their final fates, while the elements related to the upper wholes (the entire rudiment or its axes) already did so. Accordingly, at that time embryologists put in the center of a field theory a notion of an embryonic territory endowed by holistic properties. At the earliest stages, as shown by above mentioned Driesch experiments, such a territory coincided with that of an entire embryonic body, while later on a common field became segregated into a number of more local ones. The best formulation of such kind of fields was given by Brian Goodwin (personal communication): “A field is a domain of a relational order”. It is indeed a territory where all the cells perceive somehow each other and in the case of any external interventions are ready to change their presumptive fates for restoring the whole. Noteworthy, Goodwin’s definition is applicable also to


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the last version of Gurwitsch’s field. Meanwhile, non-Gurwitsch’s fields did not imply any dynamic factors and were purely descriptive. This kind of field concept was elaborated most extensively by Paul Weiss (1939). Even in such a reduced form the field concepts have been numerously attacked by representatives of a “materialistic majority” which believed that everything related to development should be explained in terms of molecules. One can distinguish two groups of such attacks. The first one came from a discovery of a chemical nature of so-called Spemann’s organizers (now usually defined as inductors) (Gilbert, 2010) that is the factors generating complicated patterns of so called axial organs out of unprepared embryonic tissue. The second came from the traditional opponents of the field theory, the genetics, whose mostly fierce adherents believed that everything within the organisms can be reduced to the action of mutually independent miniature corpuscular factors (Gilbert et al., 1996). Closer to our time both trends to a large extent merged together because the action of inductors has been interpreted in the terms of cascades of genes activation. True, the most insightful representatives of both camps had already understood long ago that neither the action of “organizers” nor the genetic effects disprove the field idea; rather, they indirectly confirmed it, although in a modified form (Waddington, 1940; Rapoport, 1996). For example, it was observed, that most of mutations spread their effects over definite territories, coinciding with those described beforehand by embryologists as the fields of organs. In other words, the organ-forming territories react to genetic mutation as holistic systems, rather than the mosaics of independent parts. So called homeotic mutations (Lewis, 1978), exchanging in the same location one organ to another (for example, an insect leg to antenna) also acted as the switchers between two discrete “wholes”, instead of affecting embryonic tissue in a mosaic way. These and other related facts brought the influential modern authors to the important conclusion that “the morphogenetic field (and not the genes or the cells) is seen as a major unit of ontogeny whose changes bring about changes in evolution” (Gilbert et al., 1996). Meanwhile, such a marriage of two harsh former opponents, the fields and the genes, led unavoidably to substantial modifications of the beforehand formulated field concepts. First, it became obvious that the action of genetic factors upon fields should be somehow introduced into the field theory. Second, the modern studies on the inductive interactions between embryonic tissues have shown that already a non-induced tissue is quite far from being a tabula rasa, containing instead in some potential form a restricted number of discrete potential pathways, each one of a holistic nature. This extensively enriches a field concept, bringing it closer to the deep Bohm’s ideas of an implicit and explicit order (Bohm, 1980). The first category of order is not enfolded in Euclidean space, while the second one is. By Bohm’s idea, the first one is more fundamental.

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At approximately the same time, a popular concept of “positional information” (PI) appeared (Wolpert, 1969, 1996) and has been regarded by many as a substitute of field concepts. It is not so, however. The PI-concept postulates the existence of some number of independent PI “sources” and “sinks”, dictating the course of development to all the other, “passive” elements of embryo. No laws of PI have been ever proposed; every next PI action is taken ad hoc. More concrete, PI concept can be shown to be incompatible with the basic phenomena of embryonic regulations (for details see Beloussov, 1998).

3. Field concept and a theory of self-organization The reasons for introducing the field concept in biology were quite different from those motivating the physicists to formulate a theory of electromagnetic or gravitational fields. While in physics the notion of field was used for describing a long range action of a signal emitted from a definite source and then passively transmitted through an “empty space”, in biology from the very beginning this notion was used for comprehending the origin of a complicated organization from something less (or even non-) organized; contrary to physics, the “action at a distance” was not an indispensable component of a biological field. Being confronted with the problem of a spontaneous (nonimprinted from outside) complication, the XIX century end biologists unexpectedly and non-deliberately turned out to be very much ahead of the contemporary physics, though still adhered to the principles of linear (one-toone) determinism. Interestingly, the latter’s principles have been reformulated in the terms of a symmetry theory (Curie, 1894) almost simultaneously with the publication of the beforehand mentioned Driesсh experiments (year 1891). It is thus understandable why Driesch considered his results as incompatible with the laws of inorganic sciences. However, at this time the first milestones were being laid by Henry Poincare (1893) and Alexander Lyapunov (see Prigogine, 1980) to the basis of what was called much later “a non-linear way of thinking”. More than half a century was passed before it was realized that these treatises, being at the first glance quite far from biology (they were related initially to celestial mechanics), created a basis for a new world view, permitting to regard self-complication as an inherent property of a large class of systems, both organic and non-organic. Closer to our time, this approach was defined as a self-organization theory (SOT) (Nicolis and Prigogine, 1977) or synergetics (Haken, 1978). Within a SOT framework, Driesch’s embryonic regulations and the top-down causation could be considered as widely spread natural events, rather than specific properties of living beings only. Remarkably, some basic ideas of SOT were formulated by biologists in a close context to the field theories even before SOT itself took a modern co-


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herent shape. This was done by Waddington (1940) who introduced the notion of chreodes, precisely translated into SOT language as the structurally stable developmental pathways. Few decades later Thom (1970) defined the morphogenetic field as a region of a phase space surrounding a chreod and giving rise to a definite morphology. Implications from SOT very much affected the content and the status of the field concept(s) in biology. So far as the crucial problem of morphogenesis – to explain a self-complication of organic shapes during development – can be at least in principle solved within a SOT framework, the “biological fields” lost the positions of new first principles becoming instead the derivatives of such fundamental SOT notions like non-linear feedbacks and parametric regulation. In a broad sense, any concept using these notions can be considered as a step towards field constructions (e.g., Chialvo, 2010). It is also worth mentioning that the self-organizing fields well may be defined within a phase space of any developmentally important variables, rather than within 3D Euclidean space only, as took place in the classical fields.

4. Perspectives of the field approach in biology and its physical foundations Although the “field approach” in biology is not generally acknowledged, its importance is today much better recognized than it was in the recent past and its usage, even if in rather vague terms, is extensively increased. It becomes ever more clear that this approach is the only one giving hope to overcome a routine view to the prolonged successions of complicated spacetemporal events (creating the very essence of the biological processes) as something given ad hoc and inaccessible to the rational explanation. As discussed elsewhere in more details (Beloussov, 2011), natural sciences know two alternative rational approaches for explaining such successions: either to postulate that they are based upon unique chains of highly specific causeeffect relations or that they obey some general nonspecific embracing invariable laws. Physical sciences, since Galileo and Newton times definitely took the second approach: when tracing the successive positions A, B, C… of a thrown stone, we look for a non-specific law embracing all of them (and potentially any others), rather than postulating the existence of different specific forces bringing a stone from A to B, then from B to C, etc. In biology, for several historic reasons for a long time the alternative ideology dominated. However, hope to discover a specific one-to-one cause-effect relation for each next step of the biological successions failed: with the refinement of experimental techniques the ambiguity of cause-effect relations (e.g., the relations between genetic or epigenetic factors on one hand and the resulted morphology on the other) increased rather than diminished. Therefore, in biology, as

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well as in the physical sciences, for reaching a rational explanation we have no other choice, than to use a law-centered approach, exemplified by a field theory. Remarkably, in spite of having at the times of its origin an obvious vitalistic flavor, this theory is now bringing biology closer to a cognitive basis of non-biological sciences. What might be, in this context, the physical foundations of the “biological” fields? Although AG did not identify his “embryonic” or “cell” fields with any one known in physics, he was not sure that his concept requires the introduction in the science of some new first principles. In one of his last papers he suggested that “the idea of a field can be probably in some future expressed in physical language” (Gurwitsch, 1947). Since then, some important steps in this direction have been made. Most important ones are associated with the notion of the “proteinmachines” (McClair, 1971; Bluemenfeld, 1983), the molecular devices transforming non-vectorized chemical energy to a vectorized mechanical one. This function is almost identical to that ascribed by AG to the cell fields in his 1944 year version (see above). The main common feature of all the proteinmachines is a considerable retardation of relaxation rate of the accumulated energy and enormous restriction (often up to one) in the numbers of freedom degrees onto which the relaxation is taking place. In addition to these shortrange molecular devices, another kind of supramolecular devices, so called low entropy machines associated with extended domains of ordered water have been recently postulated (Del Guidice et al., 2005). Both kinds of machines can be regarded as something like elementary “bricks” of the fields. It remains unsolved however how their action is effectively integrated (rather than dissipated) on much larger scales, typical for multicellular organisms. This difficulty may be at least partly surmounted by taking into consideration a universal physical factor, acting on quite different scales and able to spread its action throughout large tissue regions: the mechanical stresses. As speculated elsewhere (Beloussov, 2008a), the macroscopic and structurally stable fields can be established by the interplay of the passive (coming from outside of a given embryo part) and the active (generated within a given part) mechanical stresses. Another physical factor, somehow associated with the fields, is the ultraweak photon emission from the living tissues (Popp et al. eds., 1992). This factor is also extensively delocalized and creates regular temporal patterns, linked with physiological functions of cells. Some experiments indicate its mechanosensitivity (Beloussov, 2006). However, at the present time we are still far from creating a coherent concept of a “biological” or, in a more restricted sense, “morphogenetic” field. This will be a challenging task for the next generation of investigators.


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References Beloussov, L.V. 1968. Interpretation of a succession of morphogenetic processes in a hydroid, Obelia. Biol. Nauki 7: 21–27 (in Russian). Beloussov, L.V. and additional commentary by J.M. Opitz and S.F.Gilbert. 1997. Life of Alexander G. Gurwitsch and his relevant contribution to the theory of morphogenetic fields. Int. J. Dev. Biol. 41: 771–779. Beloussov, L.V. 1998. The Dynamic Architecture of a Developing Organism. Kluwer Acad. Publishers. Dordrecht/Boston/London. 238 P. Beloussov, L.V. 2006. Ultraweak photon emission as a tool for analyzing collective processes in cells and developing embryos. In: Biophotonics and Coherent Systems in Biology (L V Beloussov, L.V., Voeikov, V.L. and Martynyuk, V.S. eds). pp. 139–158. Springer. N.Y. Beloussov, L.V. 2008a. “Our standpoint different from common…” (Scientific heritage of Alexander Gurwitsch). Russ. J. Devel. Biol. 38: 307–315. Beloussov, L.V. 2008b. Mechanically based generative laws of morphogenesis. Physical Biology 5: 015009 Beloussov, L.V. 2011. Nomothetics and idiography in developmental biology. Theoretical Biol. Forum 104: 15–34. Beloussov, LV. and V.I. Grabovsky 2003. A geometro-mechanical model for pulsatile morphogenesis. Computer Methods in Biomech. and Biomed. Engineering 6: 53–63. Beloussov, L.V., Labas, Ju.A., Kazakova, N.I. and A.G. Zaraisky 1989. Cytophysiology of growth pulsations in hydroid polyps. J. exp. Zool. 249: 258–270. Blumenfeld, L. A. 1983. Physics of Bioenergetic Processes. Springer, Berlin. Bohm, D. 1980. Wholeness and the Implicate Order. Routledge, London. Chialvo, D.R. 2010. Emergent complex neural dynamics. Nature Physics 6: 744–750. Curie, P. 1894. De symmetrie dans les phenomenes physique: symmetrie des champs electrique et magnetique. J. de Physique Ser. 3: 393–427. De Robertis, E.M. 2006. Spemann’s organizer and self-regulation in amphibian embryos. Nat Rev. Mol. Cell Biol. 7: 296–302. Del Giudice, E and 6 coauthors.2005. Coherent Quantum Electrodynamics in Living Matter. Electromagnetic Biol. and Med. 24: 199–210. Driesch, H.,1891. Entwicklungsmechanische Studien. I. Der Werth der beiden ersten Furchungszellen in der Echinodermenentwicklung. Experimentelle Erzeugung von Theilund Doppelbildungen. Z. wissenschaftliche Zoologie 53: 160–184. Gilbert S.F., J.M.Opitz and R.A.Raff 1996. Resynthesizing evolutionary and developmental biology. Dev Biol. 173: 357-372. Gilbert, S.F. 2010. Developmental Biology. Sinauer Ass. Sunderland Mass USA. Goodwin, B. 1994. How the leopard changed its spots. Weidenfeld & Nicolson, London. Gurwitsch, A.G. 1912. DieVererbung als Verwircklichungsvorgang. Biol. Zbl. 22: 458–486. Gurwitsch, A.G. 1914. Der Vererbungsmechanismus der Form. W. Roux’ Arch Entwmech. Org. 39: 516–577. Gurwitsch A.G. 1922. Ueber den Begriff des embryonalen Feldes. W. Roux’ Arch Entwmech. Org. 51: 388–415.

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Gurwitsch A.G. 1944. A Theory of Biological Field. Sovetskaya Nauka, Moskva (in Russian). Gurwitsch A.G. 1947. The concept of “whole” in the light of the cell field theory. In: Collection of Works on Mitogenesis and Biological Field Theory (A.G. Gurwitsch ed.) pp. 141– 147. USSR Acad. Med. Sci. Printing House, Moskva (in Russian). Gurwitsch A.G. 1991. Principles of Analytical Biology and a Theory of Cellular Fields. Nauka, Moskva (in Russian). Haken, H. 1978. Synergetik. Springer-Verlag, Berlin Heidelberg New York. Harrison, R.G. 1918. Experiments on the development of the fore-limb of Amblystoma, a self-differentiating equipotential system. J. exp. Zool. 25: 413–461. Levin, M. 2012. Morphogenetic fields in embryogenesis, regeneration and cancer: Non-local control of complex patterning. BioSystems, 109: 243–261. Lewis, E.B. 1978. A gene complex controlling segmentation in Drosophila. Nature 276: 565– 570. McClare, C.W.F. 1971. Chemical machines, Maxwell’s demon and living organisms. J. theor. Biol. 30: 1–34. Mocek, R. 1974. W.Roux – H Driesch. Zur Geschichte der Entwicklungsphysiologie der Tiere ("Entwicklungsmechanik"). Fischer, Jena. Nicolis, G. and Prigogine, I. 1977. Self-Organization in Non-Equilibrium Systems. Wiley, N.Y. Opitz, J.M. and R.A. Raff 1996. Resynthesizing evolutionary and developmental biology. Dev. Biol. 173: 357–372. Poincare, H. 1893. Les methods nouvelles de la mecanique celeste., Gauthier-Villars, Paris (Dover edition, 1957). Popp, F.-A., K.H. Li and Q. Gu eds 1992. Recent Advances in Biophoton Research and its Applications. World Scientific, Singapore. Prigogine, I. 1980. From being to becoming. Freeman and Co, N.Y. Rapoport, I.A. 1996. Selected works. Nauka, Moskva (in Russian). Shih J. and Keller R. 1992. Cell motility driving mediolateral intercalation in explants of Xenopus laevis. Development 116: 901–914. Teufel, T. 2011. Whole that causes their parts: organic self-reproduction and the reality of biological teleology. Stud. Hist. Philos. Biol. Biomed. Sci. 42: 252–260. Thom, R. 1970. Topological models in biology In: Towards a theor. Biol., 3. Drafts (C.H. Waddington ed.) pp. 89–116 Edinbourgh Univ. Press, Edinbourgh. Waddington, C. H. 1940. Organizers and genes. Cambridge University Press, Cambridge. Weiss, P. 1939. Principles of development. Cambridge Univ. Press, Cambridge. Wolpert L. 1969. Positional information and the spatial pattern of cellular differentiation. J. theor. Biol. 25: 1–47. Wolpert L. 1996. One hundred years of positional information. Trends in Genetics 12: 359– 364.



Chapter 15

Endogenous bioelectric cues as morphogenetic signals in vivo Maria Lobikin and Michael Levin Center for Regenerative and Developmental Biology, and Biology Department, Tufts University, Medford, MA 02155 Abstract. Complex pattern formation requires mechanisms to coordinate individual cell behavior towards the anatomical needs of the host organism. Alongside the well-studied biochemical and genetic signals functions an important and powerful system of bioelectrical communication. All cells, not just excitable nerve and muscle, utilize ion channels and pumps to drive standing gradients of ion content and transmembrane resting potential. In this chapter, we discuss the data that show that these bioelectrical properties are key determinants of cell migration, differentiation, and proliferation. We also highlight the evidence for spatio-temporal gradients of transmembrane voltage potential as an instructive cue that encodes positional information and organ identity, and thus regulates the creation and maintenance of large-scale shape. In a variety of model systems, it is now clear that bioelectric prepatterns function during embryonic development, organ regeneration, and cancer suppression. Moreover, genetic and pharmacological modulation of the prepatterns resident in physiological networks is a powerful modality for controlling growth and form. Recent data have revealed the mechanisms by which voltage gradients are transduced into downstream transcriptional cascades. Thus, mastery of the endogenous bioelectrical signaling pathways will have transformative implications for developmental biology, regenerative medicine, and synthetic bioengineering. Correspondence/Reprint request: Dr. Michael Levin, Center for Regenerative and Developmental Biology and Biology Department, Tufts University 200 Boston Ave. Suite 4600, Medford, MA 02155, e-mail: [email protected]

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1. Introduction: Bioelectricity and the history of ‘animal spirits’ Understanding the mechanisms by which cell-to-cell communication and large-scale pattern formation are coordinated in the developing embryo is of high priority to developmental biology, regenerative medicine, and oncology. Alongside well-characterized biochemical modes of cellular communication that regulate cellular behavior during pattern formation there exists an important and powerful signaling system that is only now beginning to be understood and integrated with canonical biochemical and genetic pathways (Adams & Levin, 2013). This system of information exchange functions through bioelectrical mechanisms.

What is endogenous bioelectricity? Bioelectricity, in general, refers to signals carried by voltage gradients, ion flows and electric fields that all cells receive and generate. Bioelectricity is most well known in the context of neuronal excitation in which rapid changes in transmembrane potential (Vmem) give rise to rapid action potentials. However, long-term, steady state ion fluxes, electric fields, and pH gradients are present in all cells and across epithelial sheets. At the cellular level, transmembrane potentials result from the presence of ion channels and pumps within cell membranes that function to segregate ions in differing concentrations internally and externally. This segregation of charges gives rise to transmembrane voltage potentials, usually on the order of -50 mV. It is becoming increasingly clear that these bioelectric parameters serve functional roles in signaling pathways that control cell proliferation, differentiation and migration. Thusly, understanding how these mechanisms function is of high priority to developmental biology, regenerative medicine and cancer research. In complement to other work on electromagnetic radiation and other biophysical properties of cells, this chapter focuses on the endogenous patterning roles and signaling mechanisms of spatially-distributed and slowly-varying (resting) transmembrane potentials in living tissues.

A brief history The study of bioelectricity began long ago. Original experiments date back to the 17th century to experiments done by the Dutch biologist and microscopist Jan Swammerdan who believed that muscle contraction was caused by the flow of ‘animal spirits’ (Cobb, 2002). Swammerdan placed frog muscle into glass vessels and observed that physically irritating nerves with scissors or another instrument caused the muscles to contract. However, it wasn’t until the 18th century that evidence of ‘animal electricity’ was procured by the Italian physicist and physician, Luigi Galvani (McCaig et al,

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2005). In his famous experiments in the late 1700’s, Galvani observed that extracted frog muscles would twitch when exposed to currents produced during lightning storms. Galvani believed that the activation of these muscle movements was generated by electrical fluid carried to the muscles by nerves. This phenomenon was termed ‘Galvanism’, and is credited with being the underpinnings to the modern study of electrophysiology (Bresadola, 1998). Galvani fought most of his life to persuade skeptical colleagues that ‘animal electricity’ was a reality and it wasn’t until some 75 years later that modern experimental electrophysiology was launched by Emil du BoisReymond’s Researches on Animal Electricity (Abbott, 2008). Further experimentation conducted in the 19th century implicated electrical potentials in the process of wound healing. In 1831, Matteucci demonstrated the existence of action potentials in nerve and muscle cells for the first time by measuring injury potentials at cut ends using a galvanometer (McCaig et al, 2005). Injury potentials are now known to be a steady state, long-lasting direct current (DC) voltage gradient induced within the extracellular and intracellular spaces by current flowing into and around an injured nerve. Emil du-Bois Reymond built upon the initial observations of Matteucci by measuring current flowing out of a cut on his finger. This flow of current is due to the short-circuiting of the transepithelial potential (TEP) difference that occurs at a skin lesion (the TEP drives charged ions through the wound because the gap in the epithelium forms a low-resistance path for current flow). Human skin, as well as that of guinea pigs and amphibians, maintains a TEP across epithelial layers. When the skin is cut, a large, steady electric field (EF) arises immediately and persists for hours at the wound edge, as current pours out the lesion from underneath the wounded epithelium. This injury current is known to be essential for the regeneration of new limbs, where currents between 10 and 100 µA/cm2 create a steady voltage drop of roughly 60 mV/mm within the first 125 µm of extracellular space (McCaig et al, 2005; McCaig et al, 2009). Transcellular currents are also known to drive development and morphology. Elmer Lund carried out extensive research on electrical potentials between in the 1920s and 30s, arguing that electrical patterns are intimately related to the morphogenetic processes and vector properties of cell and tissue functions (Harold, 1982; Lund, 1947). The modern reformulation of these ideas is largely due to the work of Lionel Jaffe and his colleagues (done some 30 years later), demonstrating that electrical properties of individual cells, epithelial sheets, neural structures and limbs were necessary for growth and proper pattern and polarity establishment (Jaffe, 1981; Jaffe & Nuccitelli, 1977).

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Bioelectricity in the molecular age Several key aspects demarcate modern studies of bioelectricity from its foundations. First is an increased appreciation of spatial distribution of resting potentials. While classical works focused on electric fields and ion fluxes (mostly due to epithelia) (Borgens, 1982; Borgens, 1983; Nuccitelli, 1987; Nuccitelli et al, 1986; Robinson & Messerli, 1996; Shi & Borgens, 1995a), we now know that the spatial organization of plasma membrane voltage levels across tissues and organs carries vital patterning information that drives anatomy (Levin, 2012a). Secondly, techniques are now available for the molecular characterization of the mechanisms that both produce and respond to these gradients (Zhao et al, 2006). Together with traditional techniques such as physiological measurements and applied fields, endogenous gradients can now be manipulated with tight spatio-temporal specificity at the molecular level, via the genetic modulation of well-characterized channels and pump proteins (Adams & Levin, 2012). Thus, in addition to functional data on the electric properties themselves, the source and downstream effectors of changes in Vmem can now be dissected in great detail; for the first time, the patterning information encoded within dynamic bioelectrical networks are being integrated with well-known biochemical cascades and generegulatory networks. The results of these efforts reveal that embryonic patterning, regenerative repair and the suppression of cancerous disorganization all require continuous signal exchange between cells, tissues and organ systems (Adams, 2008; Levin, 2009).

2. The role of endogenous electric fields and voltage gradients in morphogenesis The sources of endogenous bioelectric signals are shown in Fig. 1. Modern experimental techniques to probe animal electricity have come a long way since Galvani first made dead frog muscle twitch by applying an electric current to a nerve. Using standard techniques of molecular genetics, we can now target the expression patterns of ion channels and transporters for rational modulation. The use of knockout, RNAi, or morpholinos (antisense oligonucleotides to target specific mRNA sequences) allows gene-specific loss-of-function experiments. Pharmacological blockade, while not as specific as molecular approaches, offers the benefits of temporal control of inhibition, as well as the ability to target whole groups of ion channels or pumps at once – an important feature given that multiple ion translocators of the same family are often co-expressed and can compensate for each other, thus masking important phenotypes in gene-targeting experiments (Blackiston et al, 2009).


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Figure 1. Sources of bioelectric signals at multiple levels of organization. Endogenous bioelectric signals comprise a set of biophysical properties that include voltage gradients, electric fields, and individual ion flows. In vivo, these originate at multiple levels of organization. (A) Organelle membranes generate voltage gradients, such as the nuclear envelope potential (largely unexplored) and the well-understood mitochondrial potentials. In recent years, the roles of resting potential across the plasma membrane of the cell (B) has become known as an important determinant of cell fate; spatial gradients of such voltage values over cell fields are now known as regulators of pattern formation in embryogenesis and regeneration. Decades ago it was recognized that the trans-epithelial electric field resulting from the parallel activities of polarized cell layers (C) was an important factor for guidance of migratory cell types during development and wound healing. Finally, at the level of entire appendages or even whole organisms (D), large-scale potential differences presage and control anatomical polarity and organ identity.

In the past decade, much work has begun to identify the endogenous ion conductances that are responsible for important patterning events, and the mechanisms by which cells can translate these signals into known gene regulatory networks (Levin, 2009). Conversely, exogenous ion channels or pumps can be introduced into cells and thus allow predictable changes in transmembrane potential to reveal gain-of-function phenotypes. These techniques have now been used in numerous model species to show that endogenous bioelectric gradients are among the most important sources of morpho-

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genetic information in vivo (Adams & Levin, 2012a; Levin, 2012a; Levin & Stevenson, 2012; McCaig et al, 2009; Pai et al, 2012).

Vmem as a regulator of cell behavior Morphogenesis broadly defined is the dynamic process by which the geometry and topology of complex biological structures is established. This occurs during embryogenesis, but is also important during remodeling and regeneration during adulthood. The establishment and maintenance of shape on many scales (cells, tissues, organs, and entire bodyplans) is regulated by a number of epigenetic factors controlling gene expression. Cells with different membrane and cytoplasm properties, but with identical DNA complements must consistently form and maintain various embryonic and adult structures. It has long been known that voltage gradients can mediate some of the necessary long-range communication through endogenous electric fields (Jaffe, 1981; Jaffe & Nuccitelli, 1977; Nuccitelli, 1988). More recent work has shown that targeted perturbation of transmembrane voltage results in specific, coherent changes of large-scale patterning. Remarkably, modulation of resting potential does not in itself impair embryonic viability, and it is often possible to dissociate subtle patterning functions of bioelectric states from basic housekeeping physiology of cells. Thus, Vmem levels in key groups of cells have been implicated in controlling the head-to-tail (Beane et al, 2011) and left-right (Aw et al, 2010) body axis polarity, the patterning of craniofacial structures (Vandenberg et al., 2011), the induction of eye development (Pai et al, 2012), and the initiation of Xenopus tail regeneration (Adams et al, 2007; Tseng et al., 2011). One example of how Vmem values regulate the behavior of key cell populations in vivo is demonstrated by the discovery of a set of cells in the frog embryo that can confer a neoplastic-like phenotype upon stem cell derivatives, resulting in an embryo-wide ‘hyperpigmentation’ phenotype (Blackiston et al., 2011). The expression of the glycine-gated chloride channel (GlyCl) demarcates a widely, yet sparsely distributed cell population that can be specifically targeted by exposing embryos to the potent GlyCl channel agonist, ivermectin (Ottesen & Campbell, 1994). Then, controlling the extracellular concentrations of chloride in accordance to the Goldman equation, the membrane potential of GlyCl-expressing cells can be specifically modulated to known levels (and monitored with voltage-reporting fluorescent dyes). When depolarized, these GlyCl-expressing cells instruct, over a significant distance (mediated by regulation of serotonin signaling), the neural crest cell-derived melanocytes to undergo a neoplastic-like conversion acquiring three major properties commonly associated with metastasis: they hyperproliferate, become highly invasive, and undergo a change in shape, as well as up-regulating genes associated with neoplasia – SLUG and Sox10 (Morokuma et al, 2008). Crucially, this metas-


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tasis-like phenotype can be reproduced by misexpressing mRNAs encoding sodium, potassium, or proton transporters, and can be rescued by the simple manipulation of extracellular ion content or through misexpression of opposing (hyperpolarizing) channels that drive the bioelectric state of the instructing cells back to normal. Together these data demonstrate that the control of instructor cell-derived signaling is driven by voltage per se, not necessary any one specific channel protein or type of ion.

How is Vmem change transduced into specific cellular responses? Several known mechanisms (Fig. 2) convert long-term changes in Vmem levels into second-messenger cascades that ultimately drive transcriptional responses (Levin, 2007). Voltage-driven conformational changes of molecules such as integrins (Arcangeli & Becchetti, 2010; Arcangeli et al, 1993) and phosphatases (Lacroix et al, 2011; Okamura & Dixon, 2011), as well as voltage-regulated movement of signaling molecules through calcium channels (Varga et al, 2011), gap junctions (Brooks & Woodruff, 2004; Fukumoto et al, 2005), and neurotransmitter transporters (Levin et al, 2006), can all play a role in linking biophysical events to changes in gene transcription. These processes then feed into several known genetic mechanisms, often involving changes in expression or function of genes such as PTEN, Integrin, SLUG/Sox10, Notch, SIK, and NF-kB. This, in turn, leads to changes in cell cycle, position, orientation and differentiation. It is now known that the nuclear membrane also possesses its own complement of ion transporters (Bustamante, 1994; Bustamante et al, 1995; Bustamante et al, 1994; Mazzanti et al, 2001); although the function of the nuclear envelope potential has not been explored in developmental patterning, it is possible that the current picture of bioelectric signaling needs to be expanded beyond cell surface events. Thus, Vmem changes and ion flows can function as one link in the continuous interplay between genetic networks (which establish patterns of ion channel and pump expression) and the biophysical events that redistribute signaling molecules and control cell behavior within the longrange signaling pathways that occur during development and regeneration.

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Figure 2. Mechanisms for converting membrane voltage change into transcriptional events. Multiple mechanisms exist within cells to transduce changes in Vmem (a biophysical event) into genetic responses. Transcriptional cascades are initiated by second messenger systems that are voltage-regulated, including the movement of small molecules such as serotonin (5HT) through gap junctions via electrophoresis (voltage gradient between two connected cells) or through voltage-powered transporters such as the serotonin transporter SERT. Other molecules, such as integrin receptors and voltage-sensitive phosphatases can convert changes in Vmem into powerful integrinand PTEN-dependent downstream signaling. Additional small molecules include Calcium, mediated by voltage-gated calcium channels, and butyrate/sodium transporters (such as SLC5A8) that allow voltage to control the import of key epigenetic regulators such as butyrate. Legend: star indicates membrane protein. Cloud indicates a process (chain of signaling steps). Lightning bolt indicates local change in transmembrane potential. Cylinder indicates a gap junction pore to neighboring cell. Colored circles represent small signaling molecules.


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Techniques for identifying voltage transduction mechanism How can the particular transduction mechanism mediating any bioelectric effect be identified in a specific assay? One example is provided by the identification of the ‘instructor cells’ that, when depolarized, cause a hyperpigmentation phenotype in Xenopus laevis. In this case, as well as other similar examples in vivo, the mechanism by which long-term depolarization is transduced into transcriptional and cell behavior changes was identified through a suppression drug screen. In such a loss-of-function approach, each possible signal transduction candidate is probed by inhibiting it to determine whether this suppresses a given effect of Vmem change (Adams & Levin, 2006; Adams & Levin, 2012). In the case of melanocytes, inhibitors of Ca2+ influx, of serotonin transporter (SERT) function, or of gap junctional connectivity were used together with the depolarizing ivermectin treatments. Only exposure to the specific inhibitor of the serotonin transporter (fluoxetine) blocked ivermectininduced hyperpigmentation in all of the treated embryos, suggesting that SERT is required for the transduction of this bioelectrical signal (Blackiston et al, 2011). Consistently with this model, embryos treated directly with extracellular serotonin also resulted in consistent hyperpigmentation. Similar screens have resulted in the identification of the various transduction mechanisms in various morphological events (summarized in Table 1). Developmental role Tail regeneration in Xenopus: 1° step Tail regeneration in Xenopus: 2° step Proliferation of progenitor cells

Key biophysical event Voltage change (repolarization) Intracellular sodium content Voltage change

Neoplastic conversion of melanocytes in Xenopus tadpoles Polarity determination in planarian regeneration Left-right patterning in Xenopus embryos

Voltage change (depolarization)

Trachea size control in Drosophila

Voltage change

Transduction mechanism Guidance of neural growth SIK2 (saltinducible kinase) Ca++ flux through voltage-gated calcium channels Serotonin movement through SERT Ca++ flux through voltage-gated calcium channels

Voltage change

Serotonin movement through gap junctions

Ion-independent function

Planar polarity, septate junction structure

Reference (Adams et al, 2007) (Tseng et al, 2010) (Ring et al, 2012)

(Blackiston et al, 2011; Morokuma et al, 2008) (Beane et al, 2011)

(Adams et al, 2006; Fukumoto et al, 2005a,b; Levin et al, 2002) (Paul et al, 2007)

Table 1. Known transduction mechanisms by which ion flows impact morphogenesis.

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Bioelectric signals for coordination of non-local morphogenesis Large-scale pattern formation requires the orchestration of numerous celllevel processes. Bioelectric gradients are an ideal mechanism for implementing such coordination because they function across a range of size scales (Adams & Levin, 2013; Blackiston et al, 2009; Sundelacruz et al, 2009) and control basic cell behaviors such as cell cycle progression (Binggeli & Weinstein, 1986; Sundelacruz et al, 2009) and differentiation (Konig et al, 2006; Konig et al, 2004), in a wide range of cell types, including human mesenchymal stem cells (Sundelacruz et al, 2008), embryonic stem cells (Ng et al, 2010), and mature somatic cells (Cone & Cone, 1976; Cone, 1970). Many studies have also examined the effects of Vmem on cell migration and orientation, and significant progress has been made on dissecting the molecular mechanisms driving these processes in the context of wound healing (McCaig et al, 2005; McCaig et al, 2009; Schwab, 2001; Zhao et al, 2006) and whole-body embryogenesis (Pan & Borgens, 2010; Shi & Borgens, 1995). Given the abilities of voltage gradients to exert influence both cellautonomously and over long distances, what kind of patterning information can bioelectric signals mediate? Transmembrane potential can specify issue identity at the level of cell groups (Levin, 2012) as evidenced by recent findings showing that the artificial manipulation of Vmem (hyperpolarization to a specific level) in developing Xenopus embryos can turn cell groups far from the anterior neuroectoderm to an eye fate (Pai et al, 2012). Vmem changes can also control large-scale axial polarity, such as the head-tail polarity of regenerating planarian fragments (Beane et al, 2011; Marsh & Beams, 1947; Marsh & Beams, 1952), and the left-right patterning of the early frog embryo (Levin, 2006; Levin et al, 2006). In the latter series of studies, a pharmacological screen first implicated several ion transporters in establishment of correct laterality (Levin et al, 2002); serotonergic mechanisms mediating the effect were later found using a suppression screen (Fukumoto et al, 2005; Fukumoto et al, 2005). Transmembrane voltage patterns across tissues can also provide positional information to guide migratory cells in vertebrate neurulation (Shi & Borgens, 1995) or specify the spatial patterns of gene expression during craniofacial morphogenesis (Vandenberg et al, 2011) – a kind of subtle prepattern that underlies the biochemical and genetic prepatterns that drive anatomy. In addition to providing large-scale anatomical identity and controlling the geometry of gene expression, bioelectric gradients can act as master regulators, triggering highlyorchestrated, self-limiting downstream patterning cascades such as regeneration of an entire appendage. For example, regeneration of the tadpole tail can be induced by very simple signals consisting of modulations of proton or sodium ion movement in the blastema during non-regenerative stages (Adams et al, 2007; Tseng et al, 2010).


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Given this epigenetic control of cellular processes, it should come as no surprise that bioelectric properties are essential to many developmental processes that require the proliferation, differentiation, migration and orientation of a vast number of cells. These same signals that are necessary in the regeneration and remodeling of complex tissues also participate in the continuous battle of multicellular organisms to avoid the runaway growth of cancer.

3. Endogenous electric fields & ionic flow in the detection & treatment of cancer The same signaling mechanisms required for stem cell specification and lineage restriction during embryonic pattern formation also play fundamental roles in adult tissue regeneration and cancer. Indeed, cancer can be described as a lack of morphostasis, or a disruption in the ability to maintain target morphology (Oviedo & Beane, 2009; Rubin, 1985; Tsonis, 1987).

The molecular physiology of cancer Many of the same signaling pathways (i.e. TGFß, Wnt, Notch, etc.) regulate self-renewal in both stem cell and cancerous cell types (Al-Hajj & Clarke, 2004; Bjerkvig et al, 2005; Reya et al, 2001; Wicha, 2006). While the unique bioelectrical properties of tumor tissue have long been recognized (Burr, 1941; Cameron & Smith, 1989; Koch & Leffert, 1979; Rozengurt & Mendoza, 1980), it is only in recent years that ion channels and bioelectric communication have emerged as important players in cancer-related processes. Many ion channels have been found to be involved in cancer-related cellular behaviors such as proliferation, apoptosis, migration and angiogenesis (Blackiston et al, 2011; Fiske et al, 2006; Kunzelmann, 2005; Morokuma et al, 2008; Pardo et al, 2005; Prevarskaya et al, 2007; Roger et al, 2006). In fact, ion channels are involved in each of the six traditional hallmarks of cancer: 1) self-sufficiency in growth signals, 2) insensitivity to antigrowth signals, 3) evasion of programmed cell death (apoptosis), 4) limitless replicative potential, 5) sustained angiogenesis, and 6) tissue invasion & metastasis (Hanahan & Weinberg, 2000; Prevarskaya et al, 2010). The bioelectric profiles of different cell types demonstrate the link between membrane voltage and proliferative potential. The resting Vmem’s of various cell types vary widely (generally -10 mV to -90 mV) with plastic, embryonic, stem and tumor cells being relatively depolarized, whereas quiescent, terminally differentiated cells are relatively hyperpolarized (Binggeli & Weinstein, 1986; Sundelacruz et al, 2009). Membrane potentials are involved in the control of mitosis rate, as the modulation of Vmem has been shown to be required for both the G1/S and G2/M phase transitions

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(Blackiston et al, 2009; Freedman et al, 1992). Mitotic arrest can be achieved by hyperpolarizing Chinese hamster ovary cells to -75 mV, and reversed by depolarizing to -10 mV (Cone & Tongier, 1973). Depolarization is also responsible for the hyper-proliferation of melanocytes in Xenopus embryos (Blackiston et al, 2011; Morokuma et al, 2008). Vmem thus provides a convenient target for the modulation of proliferative potential. A number of ion channels have also been implicated in enhanced cell migration, motility and invasion; all crucial components of tumor metastases. For example, voltage-gated sodium channels have been detected in biopsies of metastatic breast, prostate and cervical cancers as well as in metastatic cancer-derived cell lines (Diaz et al, 2007; Diss et al, 2005; Fraser et al, 2005; Roger et al, 2007). Potassium and chloride channels have also been implicated in the dynamic changes in cell shape and volume required for the capacity to move and invade extracellular spaces in glioma cells (McFerrin & Sontheimer, 2006; Prevarskaya et al, 2010). A number of these studies indicate that highly metastatic cancers express embryonic isoforms of voltage gated sodium channels, further supporting the notion that cancer is a recapitulation of a developmental state. More recently, two studies revealed that depolarized membrane voltage is both a physiological signature by which nascent tumors can be non-invasively detected using fluorescent reporter dyes, and a functional parameter that can be used to control tumorigenesis: artificial hyperpolarization of oncogene-expressing cells by a range of ion channel types significantly reduces the formation of tumors in an amphibian model (Chernet & Levin, 2013; Lobikin et al, 2012).

Cancer: rogue genetics or loss of tissue organization? In fact, developmental systems are a convenient model for the studies of cancer biology, providing access to a number of stem cell populations that are present throughout embryogenesis, many of which have been implicated with neoplasms. Perturbations in embryonic systems that can induce neoplastic-like phenotypes thus allow significant insights into the signaling mechanisms that may give rise to the creation of cancerous stem cells. Stem cells can be regarded as the center of the regeneration-development-cancer triad (White & Zon, 2008) and the backbone of the cancer stem cell hypothesis (Dean et al, 2005). Melanomas, for example, are tumors of pigmented cells known as melanocytes. Recent studies have highlighted that melanoma cells seem to revert to a more stem cell-like phenotype as they become more aggressive, showing decreased expression of the micropthalmia-associated transcription factor (MITF) and tyrosinase-related protein 1 (TRP1) (Bittner et al, 2000; Hendrix et al, 2003). This de-differentiation might make highly aggressive tumors more difficult to identify in routine histopathology amplifying the need for different classification standards.


Figure 3. A mind-map of the field of bioelectricity.

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There is currently significant debate as to whether stem cell disregulation and genetic mutation (Vaux, 2011a; Vaux, 2011b), or epigenetic signals from the microenvironment (Hendrix et al, 2007; Sonnenschein & Soto, 2011; Soto & Sonnenschein, 2004; Soto & Sonnenschein, 2011) are the better perspective from which to understand cancer. Importantly however, bioelectric mechanisms have now been shown as central players in both types of events (Levin, 2012b). Regardless of which view turns out to be the more accurate, continued advances in the understanding of regulation of stem and somatic cells by voltage gradients, and the interplay between biophysical and genetic regulators, are likely to have significant implications for the cancer problem.

4. Conclusion Endogenous membrane voltage are one key component of the rich set of electromagnetic events taking place in living tissues; their spatio-temporal distribution represents important, yet still under appreciated, sources of instructive information in the control of morphogenesis. Recent work, making use of modern experimental techniques, has allowed scientists to probe the connections between these biophysical signals and the molecular-genetic downstream pathways that control cell behavior and thus large-scale patterning. However, we are only beginning to scratch the surface, and much development of technology and conceptual apparatus must take place before a full understanding of self-generated order and information storage in physiological networks can be gained. This includes development of theoretical formalisms for modeling information storage in real-time physiological (not genetic) networks, comprehensive (quantitative) physiomic profiling of morphogenetic model systems in vivo, and the application of tools such as optogenetics to allow the experimental re-writing of bioelectric patterns in living tissues. Bioelectricity (Fig. 3) still represents a novel area of research in the life sciences, and improvement in the ability to control bioelectrical information is sure to be transformative for regenerative medicine, bioengineering, and synthetic biology.

Acknowledgments We thank the members of the Levin lab and the bioelectricity community for many useful discussions. M.L. is grateful for support of the NIH (awardsAR061988, AR055993, EY018168), the G. Harold and Leila Y. Mathers Charitable Foundation, and the Telemedicine and Advanced Technology Research Center (TATRC) at the U.S. Army Medical Research and Materiel Command (USAMRMC) through award W81XWH-10-2-0058.


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Chapter 16

Electromagnetic resonance and morphogenesis Alexis Pietak Department of Chemical Engineering, Queen’s University, Kingston, Ontario, Canada Abstract: Science has yet to resolve the mystery of a living being’s selfassembly into intricate patterns of form and function. Tissue patterns in developing flower buds implicate physical resonance as a mechanism through which positional information can be generated in biological development. In resonance, a trapped waveform creates a spatial pattern of a physical property. A resonance pattern is mathematically defined and is characteristic of the specific mechanism that produced the resonance. Resonances can spontaneously emerge as intricate 3D vector fields with varying strength and direction of action at every point in the involved space. As physical force fields, vector resonances can supply individual cells with positional information and influence genetic expression and other cell activities via a variety of established physical mechanisms such as voltage gated membrane channels, thereby changing cell state at specific locations to create a pattern of form and function. Remarkably, the three dimensional architectures of developing plant flower buds show striking parallels with resonance patterns formed by electromagnetic energy. Correspondence/Reprint request: Dr. Alexis Pietak, Department of Chemical Engineering, Queen’s University Kingston, Ontario, Canada. E-mail: [email protected]

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Introduction Ubiquitous yet stunning mysteries can be found in the exquisite patterns created by living beings, especially in the plant realm. For example, Figure 1A shows a cross-section through the ovary of an abutilon flower, revealing eight ova (immature seeds) that evenly divide the unit circle with a spacing of 45o (2π/8). Strikingly, the spatial characteristics of the abutilon flower ovary are described in close relation to two nested 8-point star polygons (Figure 1B). Similarly, Figure 1E shows the cross section through the male flower bud of a squash plant revealing 15 pairs of developing pollen tubes evenly dividing the unit circle with a spacing of 24o (2π/15) and exhibiting a featureless zone prescribed by a 15-point star polygon (Figure 1F). What physical mechanism can account for the appearance of such architectures? To shed light on these questions, attention must first be turned to the process of morphogenesis to appreciate the context of mysteries science has yet to elucidate.

Figure 1. Parallels between plant primordia and star polygons. Panel A shows a cross-section through an immature abutilon flower ovary. Panel B shows the same image in relation to 8-point star polygons. Panel E shows a cross-section through an immature male flower bud of a squash. Panel F shows the acorn squash primordia in relation to a 15-point star polygon. Panels C and G show the TM4,4,2 and TM15,15,2 modes (notation defined on pg. 8 and 10), respectively, with electric field direction shown as a streamlined vector, electric field magnitude as a blue gradient, and magnetic field magnitude as a red gradient. Panels D and H show the relationship between the modes and star polygons. Scale bar in images shows 250 µm.

Morphogenesis describes the development of biological form from a small collective of selfsame cells. In morphogenesis, intricate, mathematically precise natural architectures develop from simple, symmetrical initial states that show no traces of the patterns to come. An example of pattern

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emergence is given in Figure 2, where the inner compartment of a primordial squash ovary initially shows no distinct structure (Figure 2A), yet at a certain point in development a characteristic pattern defined by three sets of two spiral-like structures appears (Figure 2B). The emergence of pattern from a simple initial state is referred to as symmetry breaking. Like breaking a mirror, the initially smooth, continuous surface or shape with a high degree of symmetry is transformed into one with less symmetry due to the appearance of new structures and forms.

Figure 2. Emergence of pattern and morphostasis in female squash flower ovary primordium. Panel A shows the early primordium, Panel B shows a slightly larger primordium at a time of early symmetry breaking, and Panel C and D shows progressively more developed cross-sections of the fruit that retains aspects of the original emergent pattern. Panel E shows a three-dimensional view of the primordial female squash flower with an ovary marked by ‘*’. Scale-bars in images show 250 µm.

With continued growth and development, the pattern may remain essentially the same in its general characteristics, but is composed of first hundreds, then tens of thousands, and then millions to billions of cells in the final mature form (Figure 2C and D exemplify this for the squash ovary and fruit). This conservation of an early pattern throughout development of an organism is known as morphostasis (Levin, 2009). The mechanisms responsible for the first symmetry-breaking in the biological collective are there-

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fore of great importance as the final pattern of the mature organism is often a multicellular/multifunctional version of the initial pattern to appear in the primordial collective. It is well established that molecular signals and genetic expression play essential roles in both plant and animal development. Many years of research have focused on understanding development by inhibiting the expression of genes or the activities of their protein products. The result is an extensive list of correlations between various aspects of development and genetic expression. For example in plants, the activities of highly conserved gene families have been implicated in the formation of the four organ systems of the flower (i.e. the sepals, petals, stamens/anthers, and ovary/pistil) (Krizek et al., 2005). While the involvements of specific genetic expressions are well established in plant organogenesis, it remains largely unknown how genes are activated in certain spatial regions and how the distribution of functional biomolecules and cell types can form in dynamic spatial patterns. Ultimately, the processes involved in the symmetry-breaking of the early collective remain the greatest mysteries of morphogenesis in both plant and animal development. For morphogenetic symmetry-breaking to happen, certain cells in certain spatial regions must suddenly change their genetic expression to become cells of a different differentiation state in those regions. The cells that are changing state in the collective must receive some kind of regionally specific signal telling them to change their activities. This regionally specific signal is referred to as positional information. The concept of positional information is essentially synonymous with the concept of a morphogenetic field (Gilbert, 2006; Gilbert et al., 2000). What is crucially missing from modern developmental biology is a comprehensive understanding of how positional information is supplied to the cells, or in other words, an elucidation of the physical nature of the morphogenetic field. Various systems-focused answers have been proposed to account for the manifestation of long-range spatial organization in plant and animal development, and in reality a variety of different mechanisms may be simultaneously involved. Positional information has perhaps been most commonly considered in terms of Reaction-diffusion type mechanisms involving the emergence of a spatial pattern of a morphogenetic chemical (Koch et al., 1994; Meinhardt, 1996). Reaction-diffusion mechanisms were introduced by the renowned scientist Alan Turing (Turing, 1952) and have been developed further by a number of scientists including Koch et al., 1994 and Meinhardt, 1996. Reaction-diffusion mechanisms are important theories capable of describing chemical gradients forming in a developing organism, the shapes and colouring of sea-shells, development of reticulated vein networks, the spots and stripes on animal hides, and the appearance of divisions in the


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early stages of a developing embryo. In addition to reaction-diffusion type mechanisms, Beloussov has suggested that positional information be supplied by mechanical stress fields self-generated in a growing embryo (Beloussov, 2008; Beloussov et al., 2006). The alternative perspective presented herein sees a developing organ as a dynamic system self-trapping electromagnetic (EM) energy in a stable spatial pattern capable of supplying the cellular collective with rich positional information.

1. Dielectric resonators: Containers for light It is well established, theoretically and experimentally, that a simple drop of water can act as a resonant cavity for light (Datsyuk, 2001; Hossein-Zadeh et al., 2006). In fact, water droplets are such good containers for light that, if doped with a light-producing material such as a rhodamine dye, they can even produce coherent laser light (Kiraz et al., 2007; Lin et al., 1992). In a water-drop the situation is similar to that of the conventional laser, except instead of a resonant cavity created by mirrors, it is the air-water boundary of the spherical water-drop that reflects light back into the droplet (Datsyuk, 2001). When light traveling within a strong dielectric strikes the interface with a weak dielectric some transmission and reflection of the incident wave occurs that is dependent on the angle of incidence and the relative differences in dielectric strengths between the two materials. Therefore, if a beam of light is travelling within a spherical water-drop surrounded by air, the light can become trapped by consecutive internal reflections. If the light is of the right frequency to interfere constructively with itself along its path, an EM resonance is established in the water-drop. In order for the water-drop to behave as the resonant cavity of a laser, rhodamine dye molecules must be added to act as the optical gain medium. The concept of a water-drop resonator can be easily extended to developing plant organs (primordia), and in fact as is explored herein, there are substantial structural parallels between tissue patterns and EM resonant modes within leaf and flower primordia (Pietak, 2011a, b, 2012). At the time of first symmetry breaking, developing plant primordia are similar to a water-drop resonator as they are initially small (100 to 500 µm radius), symmetrical (spherical, ellipsoidal, or nearly cylindrical), high water content (90-98%) structures surrounded by air or a porous, low water content rind of lower permittivity (Figure 2E). These early characteristics of plant primordia establish reasonable conditions for internal reflection of EM waves within the structure, and the consequential formation of resonant modes in a frequency range between 0.25 and 5 THz. Furthermore, the fact that plant and animal cells respond in various distinct ways to electric, magnetic, and EM fields is rather well established (Cifra et al., 2011a; Levin, 2003, 2009).

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However, unlike a water-drop resonator operating at optical frequencies, plant primordia would absorb internal EM at their resonant frequencies of 0.25-5 THz, leading to high damping of the EM resonance (low Q-values).

2. The spherical dielectric resonator Modes within dielectric resonators are comprised of individual electric and magnetic field components. Each field type is a three-dimensional entity with a stable field pattern spanning the space of the resonator (Figure 3). In time, the space is alternatively filled by electric and magnetic field patterns turning over at the same frequency as the original EM wave. As two, distinct, three-dimensional spatial patterns, each with magnitude and direction at regions throughout the space, the EM resonant mode holds an incredible amount of positional information for any substrate capable of responding to it.

Figure 3. Three dimensional vector field representations of the electric (A and B) and magnetic (C and D) field components of the TM3,3,2 mode in a dielectric sphere. Panels A and C show a three dimensional vector field with respect to an isosurface of the field magnitude. Panel B shows the electric field in cross-section while panel D shows the magnetic field in long-section. Black shows areas of highest magnetic field strength and arrow sizes are scaled in proportion to field strengths.

Electromagnetic resonant modes come in transverse electric (TE) and transverse magnetic (TM) varieties. For TE modes on a sphere, the electric


309

field has no component in the radial direction, with no further restrictions on the magnetic field aside from those required by Maxwell’s Equations. In TM modes it is the opposite with no radial component of the magnetic field. The mathematical equations describing electromagnetic resonance modes in a non-magnetic dielectric sphere are described by Yadav et al., 2004. The boundary conditions determine the set of resonant frequencies for a spherical dielectric of a particular size and permittivity. For a spherical dielectric resonator, the tangential components of the electric and magnetic fields, and the normal component of the electric displacement field and magnetic flux density must be continuous across the boundary between the resonator and its surrounding environment. Applying these conditions for the various field equations of the resonator leads to the characteristic equations (Gastine et al., 1967): For TM modes:

−

For TE modes:

=

√ √

= −

1

√

√ √

.

.

[1]

[2]

Where Hn(x) are Hankel functions of the first kind (i.e. Hn(x) = Jn(x) + j Y(x)). The radius of the sphere is represented by a. The parameters k and εr are the wavenumber and relative permittivity of the dielectric sphere material, respectively. Equations [1] and [2] assume the dielectric sphere is non-magnetic (µr = 1) and surrounded by air with relative permittivity of 1. The solutions to the characteristic equations are complex frequencies: =

− ′′ .

[3]

The solutions (roots) of the characteristic equation can be indexed incrementally using the integer L. The real component of the complex frequency (fo) represents the physical resonant frequency of the dielectric sphere. The

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Alexis Pietak

imaginary component relates the radiative loss (i.e. leaking of EM energy) of the resonator in terms of the quality factor (QR) (Julien et al., 1986): =

. 2 ′′

[4]

The quality factor is the ratio of energy stored to the energy dissipated per oscillatory cycle, and indicates how well the material will resonate. In general, Q-factors less than 0.5 will not support resonances and Q-factors greater than 0.5 will. While equation [4] yields one component of the Qfactor, dielectrics also lose energy by dislocation and conduction of charge carriers under the influence of applied fields, transforming EM energy into heat. This phenomenon can be accounted for by representing the relative electric permittivity of a lossy dielectric by a complex number: ∗

=

+

!!

.

[5]

Note that the complex permittivity of water varies considerably with frequency. Over the range from 1 GHz to 7 THz the complex permittivity of water at 25 oC can be modeled using the equation (Vij et al., 2004): ∗

=

,

" + #$ %-

%

&1 + ' (%

) *+

.+

/

1 + ' (/ −

' '/

,

[6]

with ε∞ = 2.2, ε1 = 71.49, ε2 = 2.80, ε3 = 1.6, ε4 = 0.92, ω4 = 5.26 THz, τ1 = 8.31 ps, τ2 =1.0 ps, τ3 =0.10 ps, τ4 = 0.025 ps, β1 =1.0, β2 = 0.77, β3 = 0.8, δ1 = 1.0, δ2 = 1.0, δ3 = 0.9. The real component of equation [6] was used in the characteristic equations [1] and [2] to solve for resonant frequencies at specific permittivities of water-based dielectric sphere resonators in the range from 1 GHz to 7 THz. The dielectric loss tangent, tan δ, reflects the dielectric loss component of the quality factor (QD): tan 4 =

!!

=

1

5

.

[7]

The total quality factor (QT) of the non-ideal dielectric resonator can be written in terms of the radiative and dielectric loss Q-factors (Pozar, 1998; Sheen, 2008):


6

=

1

1 5

.

311

[8]

Variations in the different parameters, n, m and L, yield distinct resonant mode patterns. For the dielectric resonator, n is related to the number of field maxima between the poles of the sphere. The number of field maxima in the x-y plane (the azimuthal or ϕ-maxima) are given directly by the m parameter.

Figure 4. Cross-sectional characteristics of EM resonant modes forming on a dielectric sphere. All panels show TM modes with electric component displayed as a streamlined vector field and magnetic fields superimposed as a grayscale gradient (field direction of magnetic fields is out of the page). The numbers in the lower right corner of each panel indicate the parameters n, m, and L, respectively of the mode index as defined on page 10.

The roots of the characteristic equations [1] and [2] are indexed by the integer L. A formal way to index the modes is to label them according to their TE or TM character and by the three mathematical indices yielding the designations: TEn,m,l and TMn,m,l. The combined electric and magnetic field patterns of a sphere for TM modes with n = 1 to 3 and L = 1 to 3 are shown along the mid-plane cross-section in Figure 4.

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3. Electromagnetic resonance and plant tissue patterns As can be quickly appreciated, the seemingly mysterious relationships between features of 8- and 15- point star polygons and abutilon ovary and male squash flower bud cross-sections are shared between the TM4,4,2 and TM15,15,2 resonant modes of the sphere (Figure 1). In these plant structures, note that ova formation in abutilon and pollen-tube formation in male squash flower buds both coincide with the locations of highest magnetic field strength in the TM4,4,2 and TM15,15,2 modes, respectively (Figure 1). The tissue pattern in primordial squash ovaries from the female flower also show remarkable parallels with the TM3,3,3 mode of a spherical dielectric resonator (Figure 5). Similar to the abutilon ovary and squash male flower bud, cells in the female squash ovary in regions that correlate to highest electric field strength have changed into placental tissue cells, while the six places of high magnetic field strength are also the six places where ova form in the squash ovary (Figure 5). Also, the exterior tissue (endocarp) of the squash coincides with a region of both low electric and magnetic fields that is in correct proportion with the EM field patterns (Figure 5). In three dimensions, the electric field spirals form six consistent tubes all the way through the long axis of the egg-shaped model, which is the same pattern seen in the squash ovary. The remarkable parallels between the squash ovary and TM3,3,3 mode also suggest an important involvement of the electric field direction as the reduced symmetry of the pattern correlates to the direction of flow paths that could be induced by the electric field. Regions where electric field lines intersect are correlated with the formation of the three main axes of the squash ovary cross-section pattern (Figure 5). Conversely, where electric fields diverge, the placental lines are separated by a gap and no main axis forms (Figure 5). Features of plant primordia at the time of first symmetry breaking allow resonant frequencies and loss factors to be estimated. The emergence of patterns in various flower buds has been observed for primordia sizes ranging from radii of 150 to 500 µm. At early stages of development, the water content of primordia is on the order of 95% by mass. Furthermore, the microwave-terahertz dielectric permittivity of high water content biological tissue has been shown to be well correlated to that of pure water (Nelson et al., 2005; Sipahioglu et al., 2003).


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Figure 5. The squash ovary primordium (female flower) shows remarkable parallels to the TM3,3,3 mode of a dielectric sphere and indicates an important role for electric field direction. Panel A shows the cross section through a squash ovary primordium with primordial ovum marked by ‘*’, placental tissue line marked with ‘p’, interior tissue (endocarp) marked with ‘m’, and exterior tissue (exocarp) marked with ‘x’. Panel B shows a cross-section through the TM3,3,3 mode with electric component shown as a vector field and magnetic field as a superimposed gradient. Panel C shows streamlines of the electric field. Panel D compiles the rich information of the TM3,3,3 mode showing convergent electric field streamlines as ‘p’, regions of highest magnetic field as ‘*’, and interior and radial node regions as ‘x’ and ‘m’, respectively. Mode TM TM TM

n 3 4 15

L 3 2 2

fo (THz) 1.57 1.44 4.13

εr 4.3 4.4 3.4

QR 6.4 2.5 107.6

QD 1.95 1.95 1.60

QT 1.50 1.10 1.58

Table 1. Summary of resonant frequencies, Q-values, and water permittivities for EM modes forming on a spherical plant primordium approximated as a watery dielectric sphere with a radius of 150 µm. The parameters 'n' and 'L' represent components of the mode index (see page 10), fo is the resonant frequency, and εr represents the relative dielectric strength of the resonator. The parameters QR, QD, and QT represent the quality factor for radiative loss, dielectric loss and total loss, respectively (defined on pages 9 and 10).

Taking these factors into account and making use of equations [1] to [8], plant primordia are indicated to be far-IR resonators with the widest estimate of their operating frequencies marking a range between 0.25 and

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5 THz. While modes typically have low total Q-factors in the range of 1 to 2, Q-factors above 0.5 are able to physically resonate. The mode Q-factors are dominated by the dielectric loss of water and are relatively independent of radiation losses. The resonant frequencies and Q-factors for specific resonant modes and sizes of a spherical developing plant primordium (approximated as a water-based dielectric sphere) are summarized in Table 1.

4. Discussion In developing plant primordia, cell orientation is correlated with the direction of the electric field of EM resonant modes. Cell orientation with the electric field is assumed as an electrically polarizable entity such as a cell will align with the direction of a static or dynamic electric field. Various kinds of tissue differentiation are correlated to respective areas of high electric or magnetic fields. Generally, reproductive tissue (ova or anther) formation is coincident with sites of highest magnetic field, whereas septa/placental lines form in regions of highest electric field. The above fieldtissue correlations assume the modes are TM in character, however, if modes are TE, the relations are opposite with cells aligning in magnetic fields and reproductive tissue forming at sites of highest electric field. Overall, the structural evidence and physical uniqueness of EM mode patterns indicates developing plant organs can support EM resonances, whereby the electric and magnetic field components guide symmetry-breaking and therefore resemble the first pattern to emerge in primordia. Rich in positional information, the EM resonant mode represents a possible physical manifestation of the morphogenetic field. It is well established that electric and magnetic fields can influence cellular behaviour in development and regeneration. The direct effects of electric and magnetic fields on cell behaviour, including electric field induced cell orientation and directions of preferred growth, have been experimentally confirmed in plants and animals (Hinkle et al., 1981; Hush et al., 1991; Levin, 2009; Malho et al., 1992; Zhao et al., 1999). Weak electric fields and intercellular ion flows have also been observed to influence embryonic and stem cell differentiation states in plants and animals (Grattarola et al., 1985; Harrington et al., 1973; Levin, 2003, 2009). Biological voltage gradients and ion flow have also been associated with control of cell proliferation and controlled apoptosis via gated ion channels (Levin, 2009).


315

Figure 6. Comparison of cell life cycle characteristics and dielectrophoretic force fields of the first and second modes of spherical resonators. Panel A and C show the dielectrophoretic force field of the fundamental mode of a resonator in interphase and metaphase, respectively, with force direction shown as arrows and magnitude as a spectral colourmap with red indicating highest force strength and blue the lowest. Panel B and C show the configuration of a cell’s cytoskeleton (red) and nuclear material (blue) during interphase and metaphase, respectively.

Dynamic electromagnetic fields may be capable of altering genetic expression of cells via gated calcium ion channels (Pall, 2013). Weak magnetic fields have been observed to induce changes in ion flux through cell membranes and altered cell growth characteristics (Blackman et al., 1994; Lednev, 1991). Furthermore, dielectrophoresis describes force exerted on a dielectric particle when it is in the vicinity of a non-uniform static or timevarying electric field (Pohl, 1978). Dielectrophoresis may alter the threedimensional distribution of morphogenetic substances in relation to a static or dynamic electric field, leading to changes in cell differentiation state and other features such as proliferation rates. A confirmation of the EM excitation source for the developing plant remains beyond the scope of this work. However, it is possible to speculate on various possibilities. The prospect of endogenous EM production by biological systems, in a range from 1011 to 1012 Hz (the same range expected for mode frequencies in developing plants, Table 1) was first developed by physicist Herbert Fröhlich in 1968 (Fröhlich, 1968a, b, 1975). More recently, quantum field theory (QFT) has been used to further elucidate the possibility of endogenous radiation in biological systems (Del Giudice et al., 1985; Del Giudice et al., 2010; Preparata, 1995). Recent explorations using normal mode analysis of simulated microtubule structures have determined micro-

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tubules to be a likely source of endogenous EM up to several hundred gigahertz (Cifra et al., 2011b; Deriu et al., 2010). Microtubule vibrations may be stimulated by hydrolysis of GTP, motor protein-microtubule interactions, and energy efflux from mitochondria (Cifra et al., 2011b). A number of experiments have directly detected non-thermal radiation in radio (Cifra et al., 2008; Pokorny et al., 2001), microwave (Gebbie et al., 1997), and visible-UV (Popp et al., 2002; Yan et al., 2005) frequencies from various organisms. The mainstream view of morphogenesis was established in 1952 by Alan Turing, who outlined how cellular collectives could generate bio-similar patterns of spots, stripes, and spirals using interrelated reaction-diffusion of chemicals in the cellular population (Turing, 1952). There are a number of issues that make chemically based reaction-diffusion mechanisms an unlikely explanation for the patterns in developing plants described herein. The main reason for this is because the phenomenon of chemical diffusion has fundamentally different physical properties, and therefore fundamentally different mathematical relations and stable patterns, than do electromagnetic (vector) resonances. Diffusion and reaction-diffusion are scalar processes exhibiting first-order changes in time, whereas electromagnetic resonances are vector properties demonstrating second-order changes in time. To date no reaction-diffusion mechanism has been identified that is capable of generating the three dimensional vector resonance patterns outlined in this paper. Furthermore, in a reaction-diffusion mechanism, the positional information would ultimately be generated by a spatial pattern of at least two chemical substances that change cell state in different ways and were patterned according to the two vector harmonic components representing the electric and magnetic fields of an EM resonance. To even form similar vector fields, there would need to be some presently unknown mechanism that acts as a vector property and can demonstrate vector curl. Finally, in plant patterns there are occasions when a similar pattern occurs but with a higher symmetry. On account of this rather common observation, the reaction-diffusion mechanism would need to have changes to the value of some parameters, such as a slower movement or reaction rate of a chemical, creating the appearance of the next harmonic in a resonance series. In other words, the reaction-diffusion mechanism would need to mimic a resonance mechanism, whereby a higher resonance frequency can generate a similar pattern but with higher symmetry. It is not clear how this would happen for a reaction-diffusion network. The concept of a biological EM resonator can be extended to diverse size scales on the order of individual cells and whole planets. Popp and Cifra have independently explored the concept of single cells as EM resonators (Cifra, 2012; Popp et al., 2005). As suggested in Figure 6, the presence of an electromagnetic mode within the cell can account for patterns in cytoskele-


317

tal filaments and genetic material (nucleus, chromosomes) during various stages of the cell life-cycle (Cifra, 2012; Popp et al., 2005). On very large scales, it has been experimentally established that even our planet Earth can act as a cavity resonator (Balser et al., 1960). The Earth is an approximately spherical entity surrounded by an electrically conductive spherical ionosphere consisting of electrons and charged atoms and molecules, which behaves as a conductor surrounded by the vacuum of outer space. The spherical ionosphere acts like an EM resonant cavity surrounding the Earth. The Schumann resonances of Earth are experimentally verified low frequency resonances (beginning at ~7.8 Hz) excited by lightning discharges in the cavity formed by the Earth's surface and the ionosphere (Balser et al., 1960). Experiments are required to determine if physiologically relevant endogenous electromagnetic resonances exist in plants and other organisms. Two core experimental strategies would involve i) attempts to directly detect EM produced by a developing plant in a range from 0.5 to 2 THz, beyond the scale expected from thermal background radiation, and ii) growing plants in the presence of an EM source emitting in the range of 0.5 to 3 THz in an attempt to alter or disrupt growth patterns in a manner consistent with the hypothesis of EM resonance and avoiding significant heating of the tissue in the process.

5. Conclusions The spontaneous self-assembly of a living being into intricate patterns of functional form largely remains a mystery. While most current research seeks explanations in terms of genetic and molecular activities, a viable alternative view already exists. For living organisms have the capacity to behave as EM resonators, trapping within themselves EM fields in the form of spatial energy patterns. These patterned energy fields are called resonant modes and are a rich source of long-range information capable of guiding biological pattern formation from an early developmental stage. Focusing on plants, the so-called living crystals of the world, a comparison of tissue and EM resonant mode patterns reveals striking similarities. The concept of EM energy resonators is not limited to plants, but may extend to single cells, water droplets, other organisms, and whole planets.

References Balser M, Wagner C. 1960. Observations of Earth-Ionosphere Cavity Resonances. Nature 188:638-641. Beloussov L. 2008. Mechanically based generative laws of morphogenesis. Phys Biol 5:015009.

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Beloussov L, Grabovsky V. 2006. Morphomechanics:goals, basic experiments and models. Int J Dev Biol 50:81-92. Blackman C, Benane S, House D. 1994. Empirical test of an ion parametric resonance model for magnetic field interations with PC-12 cells. Bioelectromagnetics 15:239-260. Bures J. (2009) Guided Optics: Optical Fibers and All-Fiber Components. Wiley-VCH, Weinheim. Cifra M. 2012. Electrodynamic eigenmodes in cellular morphology. BioSystems. 109:356366. Cifra M, Fields J, Farhadi A. 2011a. Electromagnetic cellular interactions. Prog Biophys Mol Biol 105:223-246. Cifra M, Havelka D, Deriu M. 2011b. Electric field generated by longitudinal axial mocrotubule vibration modes with high spatial resolution microtubule model. J of Phys Conf Series 329:012013. Cifra M, Pokorny J, Jelinek F, Hasek J. 2008 Measurement of yeast cell electrical oscillations around 1 kHz PIERS Proceedings, Cambridge, USA, pp 780-785. Datsyuk V. 2001. Optics of microdroplets. J Molecular Liquids 93:159-175. Del Giudice E, Doglia S, Milani M. 1985. Quantum field theoretical approach to the collective behaviour of biological systems. Nuclear Physics B B251:375-400. Del Giudice E, Spinetti P, Tedeschi A. 2010. Water dynamics at the root of metamorphosis in living organisms. Water 2:566-586. Deriu M, Soncini M, Orsi M, Patel M, Essex J, Montevecchi F, Redaelli A. 2010. Anisotropic elastic network modeling of entire microtubules. Biophys J 99:2190-2199. Fröhlich H. 1968a. Bose condensation of strongly excited longitudinal electric modes. Physics Letters A 26A:402-403. Fröhlich H. 1968b. Long-range coherence and energy storage in biological systems. International J of Quantum Chemistry 2:641-649. Fröhlich H. 1975. Evidence for bose condensation-like excitation of coherent modes in biological systems. Physics Letters A 51A:21-22. Gastine M, Courtois L, Dormann J. 1967. Electromagnetic resonances of free dielectric spheres. IEEE Trans Microwave Theory and Techniques MTT-15:694-700. Gebbie H, Miller P. 1997. Nonthermal microwave emission from frog muscles. Int J Infared and Millimeter Waves 18:951-957. Gilbert S. (2006) Developmental Biology. Sinauer Associates, New York. Gilbert S, Sarkar S. 2000. Embracing complexity: Organicism for the 21st century. Devel Dyn 219:1-9. Grattarola M, Chiabrera A, Vlvianl R, Parodl G. 1985. Interactions between weak electromagnetic fields and biosystems: A summary of nine years of research. Electromag Biol Med 4:211-226. Haken H, Wunderlin A, Yigitbasi S. 1995. An introduction to synergetics. Open Systems and Information Dynamics 3:97-130. Harrington D, Becker R. 1973. Electrical stimulation of RNA and protein synthesis in the frog erythrocyte. Exp Cell Res 76:95-98. Hinkle L, McCaig C, Robinson K. 1981. The direction of growth of differentiating neurons and myoblasts from frog embryos in an applied electric field. J of Physiology 314:121135.


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Pietak A. 2011a. Electromagnetic Resonance in Biological Form: A Role for Fields in Morphogenesis. J Phys Conf Series. 329: 012012. Pietak A. 2011b. Endogenous electromagnetic fields in plant leaves: a new hypothesis for vascular pattern formation. Electromag Biol Med 30:93-107. Pietak A. 2012. Structural Evidence for Electromagnetic Resonance in Plant Morphogenesis. BioSystems. 109:367-380. Pohl H. (1978) Dielectrophoresis: The behaviour of neutral matter in nonuniform electric fields. Cambridge University Press Pokorny J, Hasek J, Jelinek F, Saroch J, Palan B. 2001. Electromagnetic activity of yeast cells in the M phase. Electro and Magnetobiology 20:371-396. Popp F, Walburg M, Schlebusch K, Klimek W. 2005. Evidence of light piping (meridian-like channels) in the human body and nonlocal emf effects. Electromag Biol Med 24:359374. Popp F, Yan Y. 2002. Delayed luminescence of biological systems in terms of coherent states. Physics Letters A 293:93-97. Pozar D. (1998) Microwave Engineering 2nd Ed. Wiley, New York. Preparata G. (1995) QED Coherence in Matter. World Scientific, London. Righini G, Dumeige Y, Feron P, Ferrari M, Nunzi Conti G, Ristic D, Soria S. 2011. Whispering gallery mode microresonators:Fundamentals and applications. Rivista del Nuovo Cimento 34:435-449. Schneider M, Evenson K, Johns J. 1996. Far-infrared continuous-wave laser emission from H20 and from NH3 optically pumped by a CO laser. Optics Letters 21:1038-1039. Sheen J. 2008. Losses of the parallel-plate dielectric resonator. IET Microwaves, Antennas and Propagation 2:221-228. Sipahioglu O, Barringer S. 2003. Dielectric properties of vegetables and fruits as a function of temperature, ash, and moisture content. Food Eng Phys Prop 68:234-239. Turing, AM. 1952. The chemical basis of morphogenesis. Philosophical Transactions Royal Society London B. 37-72. Vahala K. 2003. Optical microcavities. Nature 424:839-846. Vij J, Simpson D, Panarina O. 2004. Far infrared spectroscopy of water at different temperatures: GHz and THz dielectric spectroscopy of water J Molecular Liquids 112:125135. Yadav R, Singh I. 2004. Normal modes and quality factors of spherical dielectric resonators: I - Sheilded dielectric sphere. Pramana 62:1255-1271. Yan Y, Popp F, Sigrist S, Sclesinger D, Dolf A, Yan Z, Cohen S, Chotia A. 2005. Further analysis of delayed luminescence of plants. J Photochemistry and Photobiology B 78:235-244. Zhang J, Grischkowsky D. 2003. Whispering gallery mode cavity for terahertz pulses. J Optics Society America: B 20:1894-1904. Zhao M, Forester J, McCaig C. 1999. A small, physiological electrial field orients cell division. PNAS 96:4942-4946.

Epilogue We always had the reader in mind who should be guided into a landscape that is partly familiar and partly not. We hope to have delivered together with all these wonderful contributions a work that was worth the effort. We are aware that today’s description of the potential and properties of biological electrodynamic fields may tomorrow be altered or extended by additional properties (for example, entanglement can be understood as a property of that field). There are an increasing number of researchers of that new field and perhaps this reflects a revolution that is taking place in biology, namely the deep (rather than only superficial) inclusion of physical fields to explain the structures and processes of living organisms. We face great questions in biology. How could cells arise? Why do they divide? How is the tremendous dynamic complexity within and between cells organized? In what way, and by what mechanisms are cells intelligent? We already ask some of these questions and we also have answers to some of them. Whether these answers are correct depends also on the framework of our thoughts – our theories and models – because it determines how we build an experimental set-up. Taking cell fields into account changes the way we build up an experiment and also of how we interpret previous studies that did not test for a confounding effect from cell fields. We wish this book to be part of a transition phase from a world-view where we shift from narrow cause-effect methodology to a larger view of the process where cause and effect become part of the same feedback system in the process of self-building. In that sense, we suggest to give up asking what was first, the chicken or the egg, but rather to study how the two evolve.

Acknowledgements The concept of this book was developed in 2011 and the editing work began in 2012. Great thanks go to the Publishing House who gave us time for developing the project and gently reminded us to stay in contact with it. The work on the book was a great experience. Here we would like to thank all authors for their readiness to cope with our recommendations and critical comments. A very great thank you goes here also to Nancy Berverly (New York) for her careful work of proofreading the chapters. We also thank the Freiwillige Akademische Gesellschaft (FAG) of Basel for financing the open access publication of this book.

Fields of the Cell

Fields of the Cell

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