Equilibrium and far-from equilibrium states

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

i

Introduction

ii

Chapter 1 The evolution of the biological field concept Antonios Tzambazakis

1

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

29

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

55

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

71

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

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

113

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

131

Chapter 8 Electromagnetic cell communication and the barrier method Daniel Fels

149

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

163

Chapter 10 Cellular electrodynamics in kHz–THz region Michal Cifra

189

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

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

247

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

271

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

283

Chapter 16 Electromagnetic resonance and morphogenesis Alexis Pietak

303

Epilogue

321

Acknowledgements

321

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. 71–94.

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

72

Claudio Rossi et al.

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

Energy flow and dissipative structures

73

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

74


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 and 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


75

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

76


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)


77

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).

Where ∆H is the Enthalpy (or the system’s energetic term) and T ∆S is the entropic term (product of temperature and entropy). 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 is due to the fact that water-molecules are more ordered with respect to parental gas-molecules – so is ice more ordered than water. 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) radiation and since that is electromagnetic in nature, water as coupled oscillators is a net IR-absorber. 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.

78


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 case, 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-


79

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 ubiquitous in any system and again act at all levels. 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.

80


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).


81

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

82


past experiences entitles a cell to 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 a shock-stimulus induces 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).


83

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 increases, 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

84


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.


85

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

86


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


87

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

88


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 as phenotypes; 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 communication-pathways 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 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 organismic of electromagnetic radiation in cell signalling: i) cellular responses to externally generated EM-perturbations, 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,


89

1983). The 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 does not occur randomly, the mediations of signs & signals are coupled to mass 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 system-theoretical 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).

90


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 new configuration. Order and disorder in biological systems (c) are expressed as rations 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 prevalent in all evolutionary processes.


91

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.

References Bateson, G. 2000. Steps to an ecology of mind. 2nd ed. University of Chicago Press, Chicago. Belousov, B.P. 1958. A periodic reaction and its mechanism, Sbornik Referatov po Radiatsonno Meditsine, Moscow (in Russian) Medgiz,: 145-147. Bolterauer, H., Tuszynski, J.A. and Sataric, M.V. 1991. Fröhlich and Davydov regimes in the dynamics of dipolar oscillations of biological membranes. Phys. Rev. A, 44: 13661381. Brizhik, L., Cruzeiro-Hansson, L. and Eremko, A. 1998. Influence of electromagnetic radiation on molecular solitons. Journal of Biological Physics, 24(1): 19-39. Brizhik, L.S. and Eremko, A.A. 2003. Electromagnetic Biology and Medicine. Taylor and Francis, 22(1): 31-39. Bush, J.W.M. 2010. Quantum mechanics writ large PNAS 107(41) 17455-17456. Castets, V., Dulos, E., Boissonade, J. and DeKepper, P. 1990. Experimental evidence of a sustained standing Turing-type nonequilibrium chemical pattern. Phys. Rev. Lett. 64(24): 2953-2956. Cifra, M., Pokorny, J., Havelka, D. and Kucera O. Electric field generated by axial longitudinal vibration modes of microtubulo. 2010. Title missing. Biosystems, 100: 122–31. Cifra, M., Fields, J.Z. and Farhadi, A. 2011. Electromagnetic cellular interactions. Prog Biophys Mol Biol, 40(6): 747-759. Deutsch, L., Folkea, C. and Skånberg, K. 2003. The critical natural capital of ecosystem performance as insurance for human well-being. Ecological Economics, 44(2-3): 205– 217. DeWitt, A. 1972. Spatial Patterns and Spatiotemporal Dynamics in Chemical Systems, John Wiley & Sons, Inc. Darwin, C. 1859. On the Origin of Species, 1st ed. John Murray, London. (accessed: 2013: http://en.wikisource.org/wiki/On_the_Origin_of_Species_%281859%29)

92


Epstein, R.I. and Pojman, J.A. 1998. An introduction to nonlinear chemical dynamics: oscillations, waves, patterns, and chaos. Oxford University Press, New York. Facchini, A., Rossi F. and Mocenni, C. 2009. Spatial recurrence strategies reveal different routes to Turing pattern formation in chemical systems, Physics Letters A, 373: 42664272. Falkner, R., Priewasser, M. and Falkner, G. 2006. Information Processing by Cyanobacteria during Adaptation to Environmental Phosphate Fluctuations. Plant Signal Behav. 1(4): 212–220. Fels, D. 2009. Cellular Communication through Light. PLoS ONE, 4(4): e5086, 1-8. Fraser, A. and Frey, A.H. 1968. Electromagnetic Emission at Micron Wavelengths from Active Nerves. Biophysical Journal, 8: 731-734. Fröhlich, H. 1970. Long Range Coherence and the Action of Enzymes. Nature, 228: 1093. Fröhlich, H. 1975. The extraordinary dielectric properties of biological materials and the action of enzymes. PNAS, 72(11): 4211-4215. Fröhlich, H. and Kremer, F. (eds.). 1983. Coherent Excitations in Biological Systems. Springer, Berlin, Heidelberg, New York. Fröhlich, H. (ed.) 1988. Biological Coherence and Response to External Stimuli. Springer, Berlin, Heidelberg, New York. Fulka, H., Mrazek, M., Tepla O., Fulka, J. (2004). DNA methylation pattern in human zygotes and developing embryos, Society for Reproduction and Fertility, Vol. 128: 14701626. Glansdorff, P. and Prigogine, I. 1971. Thermodynamic Theory of Structure, Stability, and Fluctuations. Wiley-Interscience, New York. Goldbeter, A. 1996. A, Biochemical Oscillations and Cellular Rhythms: The molecular bases of periodic and chaotic behaviour. Cambridge University Press, Cambridge. Goldbeter, A. 2007. Biological Rhythms as Temporal Dissipative Structures. In: Rice, S.A. (ed) Special Volume in Memory of Ilya Prigogine: Advances in Chemical Physics, 135: 254-295. Gryder B.E., Nelson, C.W., and Shepard. S.S. 2013.Biosemiotic Entropy of the Genome: Mutations and Epigenetic Imbalances Resulting in Cancer. Entropy, 15(1): 234-261. Harris, D.M. and Bush, J.W.M. 2013. The pilot-wave dynamics of walking droplets. Physics of Fluids 25: 091112-1-2. Harris, D. M., Moukhtar, J., Fort, E., Couder, Y. and Bush, J.W.M. 2013. Wavelike statistics from pilot-wave dynamics in a circular corral. Physical Review E 88(1): 1-5. Hecht, E. 1994. Physics – Algebra / Trig. Brooks/Cole Publishing Comp. Pacific Grove. Holtan, S.G., Creedon, D.J., Haluska, P. and Markovic, S.N. 2009. Cancer and Pregnancy: Parallels in Growth, Invasion, and Immune Modulation and Implications for Cancer Therapeutic Agents. Mayo Clin Proc. 84(11): 985-1000. Jelinek, F., Cifra, M., Pokorny, J. Hašek, J., Vaniš, J., Šimša, J. and Frýdlová, I. 2009. Measurement of Electrical Oscillations and Mechanical Vibrations of Yeast Cells Membrane around 1 kHz. Electromagn Biol Med, 28: 223-232. Kondepudi, D., and Prigogine I. 1998. Modern Thermodynamics: From Heat Engines to Dissipative Structures. Wiley Publishers, New York.


93

Koutroufinis, S.A. 2008. Organismus als Prozess. Habilitation thesis handed in at the Institute for Philosophy and Science History (in German), Technical University Berlin, Berlin. Madl P, Antonius, A. and Kleemann, K. 2005. The silent sentinels – the demise of tropical coral reefs. BUFUS 32-34; available online: http://biophysics.sbg.ac.at/reefs/reefs.htm (accessed March 2013). Madl, P. and Yip, M. 2007. Information, Matter and Energy – a non-linear world view. In: In: Witzany, G. (ed). Proceedings of Biosemiotics 6. Umweb Publ., 217-225. Madl, P., Del Giudice, E., Voeikov, V.L., Tedeschi, A., Kolarc, P., Gaisberger, M. and Hartl A. 2013. Evidence of Coherent Dynamics in water droplets of waterfalls. Water, 5: 5768. Madl, P. and Witzany, G. 2014. How Corals coordinate and organize: an ecosystemic analysis based fractal properties. Ch.20 in: Witzany, G (ed.) Biocommunication in Animals. Springer, New York, 351-382. Manzelli, P. 2007. What means life. In: Witzany, G. (ed). Proceedings of Biosemiotics 6. Umweb Publ., 227-236. McCaig, C.D., Song, B. and Rajnicek, A.M. 2009. Electrical dimensions in cell science. AM. J Cell Sci., 122: 4267-4276. Molácek, J. and Bush J.W.M 2013. Drops walking on a vibrating bath: towards a hydrodynamic pilot-wave theory. J. Fluid Mech. 727:, 612-647. Murray, J.D. 2002. Mathematical biology, Springer (2nd printing), Berlin. Nicolis, G. and Prigogine I. 1977. Self-organization in non-equilibrium systems. Wiley Publishers, New York. Nicolis, G. and Prigogine, I. 1989. Exploring Complexity. W.H.Freeman and Co., New York. Peacocke, A. 1983. The Physical Chemistry of Biological Organization, Clarendon Press, Oxford. Penrose, R. 2004. The Road to Reality – a complete guide to the laws of the Universe. A.A.Knopf, New York. Pietschmann, H. 2013. Das Ganze und seine Teile – Neues Denken seit der Quantenphysik. Iberia, European University Press (in German), Vienna. Pokorný, J. 2003. Electromagnetic Biology and Medicine. Taylor and Francis, 22(1): 15-29. Pokorný, J., Hasek, J. and Jelinek, F. 2005. Electromagnetic Biology and Medicine. Taylor and Francis, 24; 185-197. Pollack, G.H. 2013. The fourth phase of water – Beyond solid, liquid and vapor. Ebner & Sons Publ. Seattle. Popp, F.A. 1992. Essential Questions and Probable Answers; Ch.1; in: Popp, F.A., Li, K.H. and Gu Q. (eds) Recent Advances in Biophoton Research and its Applications; World Scientific Publishing, Singapore. Prigogine, I. and Stengers, I. 1979. La nouvelle alliance, Gallimard, (in Fernch) Paris. Prigogine, I. 1980. From Being To Becoming. W.H.Freeman, New York. Prigogine, I. 1991. The arrow of time. In: Ecological Physical Chemistry, Rossi, C. and Tiezzi, E. (eds). Elsevier, 1-24. Reik, W., Dean, W., Walter, J. 2001. Epigenetic reprogramming in mammalian development. Science., 293: 1089-1093.

94


Reik, W., Walter, J. 2001. Genomic imprinting: parental influence on the genome. Nature Reviews Genetics, 2: 21-32. Rossi, C., Foletti, A., Magnani, A. and Lamponi, S. 2011. New perspectives in cell communication: Bioelectromagnetic interactions. Seminars in Cancer Biology 21: 207-214. Sang-Wook, Han., Sriariyanun, M., Lee, S.W., Sharma, M., Bahar, O., Bower, Z. and Ronald P.C. 2011. Small Protein-Mediated Quorum Sensing in a Gram-Negative Bacterium. Plos One, 6(12): e29192. Scholkmann, F., Fels, D. and Cifra, M. 2013. Non-chemical and non-contact cell-to-cell communication: a short review. Am. J. Transl. Res., 5(6): 586-593. Slawinski, J. 2003. Biophotons from stressed and dying organisms: Toxicological aspects. Indian Journal of Experimental Biology, 41: 483-493. Slawinski, J. 2005. Photon emission from perturbed and dying organisms: biomedical perspectives. Forsch Komplementärmed Klass Naturheilkd., 12(2): 90-95. Thayer, J.F., Ahs, F., Fredrikson, M., Sollers, J.J. and Wager, T.D. 2012. A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev., 36(2): 747-756. Turing, A.M. 1952. The Chemical Basis of Morphogenesis, Philos. Trans. R. Soc. London Ser. B., 237(641): 37-72. Tyndall, J. 1869. Natural Philosophy in Easy Lessons - a physics book intended for use in secondary schools. Cassell, Petter & Galpin, London. Vitagliano V. 1990. Appunti sulla termodinamica dei processi irreversibili, Liguori Editore (in Italian), Napoli. Vos, M.H., Rappaport, J., Lambry, J.C., Breton, J. and Martin, J.L. 1993. Visualisation of coherent nuclear motion in a membrane protein by femtosecond spectroscopy. Nature, 363: 320-325. Waddington, C. H. 1957. The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology. Ruskin House, Allen & Unwin, London. Wind-Willassen, Ø., Molacek, J., Harris, D.M. and Bush J.W.M. 2013. Exotic states of bouncing and walking droplets. Physics of Fluids, 25: 08202-1-11. Winfree, A.T. 1972. Spiral Waves of Chemical Activity. Science, 175: 634-636. Withehead, A.N. 1978. Process and Reality – an Essay in Cosmology. The Free Press, New York. Zhabotinsky, A.M. 1964. Periodic liquid phase reactions, Proc. Acad. Sci. USSR, 157: 392395. Zhang, J., Li, D., Chen, R. and Xiong, Q. 2013. Laser cooling of a semiconductor by 40 Kelvin. Nature, 493(7433): 504-508.