Editorial to the Special Issue on Computational Systems ... - IEEE Xplore

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Editorial to the Special Issue on Computational Systems Biology BY STEPHEN T. C. WONG

Guest Editor MACIEJ OGORZALEK, Fellow IEEE

Guest Editor JOSEPH CHANG

Guest Editor

E

arly studies in systems biology focused on quantitative modeling of enzyme kinetics and the simulation systems developed to study biological mechanisms and signaling pathways, using techniques borrowed from other disciplines such as control theory and cybernetics. Owing to the constraints of sample preparation and acquisition technology, these studies often could Computational Systems only address single, or at best a few, Biology seeks to integrate pathways of the biological systems many levels of biological under investigation; and this results information derived in their limited predictability and from high-throughput reproducibility of research findings. biological experiments in With the arrival of functional genomics and high-throughput methods of order to understand how quantifying biologic processes in the complex biological 1990s means that large quantities of systems function at a high-quality heterogeneous biological systems level. data now become readily available, and this phenomenon has renewed the interests of scientific communities in developing new mathematical and computational approaches to optimize the analysis, interpretation, and modeling of such large volumes of life-science data. Computational systems biology (CSB) is a relatively new field that has recently emerged to fill the need in analyzing biological complexity and to decode how biological systems function. CSB aims to understand how interrelated components of a biological system comprise modules or networks Digital Object Identifier: 10.1109/JPROC.2008.925412

0018-9219/$25.00 Ó 2008 IEEE

whose functional properties emerge as definable and quantitative phenotypes, away from the traditional approach on a small number of components. It seeks to integrate many levels of biological information derived from high-throughput biological experiments in order to understand how complex biological systems function at a system level. By delineating the relationships and interactions between various parts of a biological system, be it a metabolic pathway, organelle, cell, physiological system, or organism, the field attempts to develop eventually a comprehensive model of the whole organism or biological system, a key both for understanding of life and for the development of predictive, preventive, and personalized medicine. The research of systems biology often generates mechanisms models of biological systems, such as the reconstruction of dynamic systems from quantitative properties of their biological building blocks [1]Y[3]. For example, a cellular network can be modeled computationally using methods coming from chemical kinetics

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and control theory. Sophisticated numerical and computational techniques are then deployed to cater for large numbers of parameters, variables, and constraints in cellular networks. New research directions are also being explored to address the development of biomarkers and surrogate markers for systems biology applications, such as biochip design in novel life-science applications, noninvasive image biomarkers for drug development and personalized medicine, biosensors and biosensor networks for systems monitoring, and high-throughput biotechnologies for drug and genetic screening [4], [5]. The research conducted along these new directions not only demands good understanding of biological mechanisms under investigation but also requires in-depth expertise in computational science and mathematical modeling. The theoretical analysis will be driven by experimental evidence or be linked closely to underlying biological studies, whereas the experiments are guided quantitatively by a systematic simulation of a biological system and are eventually used to validate the model structure and to identify the model parameters. CSB thus is inherently interdisciplinary and can be served as a dual-edged tool in facilitating both hypothesis-driven experiments and hypothesis-generating studies in life sciences. This timely Special Issue invites international leaders of computational systems biology to present stateof-the-art technologies and methods of their research as well as share their perspectives on the outlooks and trends of this fast-evolving field. Its purpose is to provide the theoretical foundations of CSB and discuss their range of applications in life sciences, including computational models and structures of biological systems, mathematical algorithms in analyzing and integrating life-science data of various biological levels, and systems solutions to generate new knowledge of biology and to understand the diseases. 1250

The Special Issue consists of 12 invited papers from leading experts around the globe. The first three papers describe system simulation at the cellular level by integrating different components of cells. Understanding of logic and dynamics of gene-regulatory and biochemical networks is a major challenge of systems biology. To facilitate this research topic, Funahashi et al. authored BCellDesigner 3.5: A Versatile Modeling Tool for Biochemical Networks.[ CellDesigner enables users to create networks on-the-fly through defined and comprehensive graphical representation. Physics-based simulation is needed to understand the function of biological structures and can be applied across a wide range of scales, from molecules to organisms. Schmidt and Altman wrote BThe Simbios National Center: Systems Biology in Motion[ to address issues of physicalbased system integration and simulation, where the system is governed by the interplay of classical forces but the motion is distributed smoothly through the materials and fluids, requiring the use of continuum methods. On the other hand, in BMultigraph Conditions for Multistability, Oscillations, and Pattern Formation in Biochemical Reaction Networks,[ Craciun and Mincheva studied the interactions among biochemical species using a directed multigraph. Network properties, such as multistability or oscillations in the existence of a positive feedback cycle, can be generalized to subnetworks. They also derived corresponding graph-theoretic conditions for pattern formation for the respective reaction-diffusion models and presented as an example a model for cell cycle and apoptosis along with bifurcation diagrams and sample solutions that confirm the predictions obtained using the multigraph network conditions. The next four papers introduce the latest work in protein and imaging biomarker discovery as well as validation. A biomarker can be defined as a characteristic that can be measured

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and evaluated as an indicator of normal biologic processes, pathologic processes, or pharmacologic responses to therapeutic intervention. Differential expression of proteins in health and disease holds the key to early diagnosis and accelerated drug discovery, as stated by Dowsey and Yang in BThe Future of Large-Scale Collaborative Proteomics.[ Their approach, however, has also brought an explosion of data complexity not mirrored by current progress in proteome informatics. This paper describes the state-of-the-art proteomics workflows, including two-dimensional gel electrophoresis, liquid chromatography, and mass spectrometry, and their utilization by the participants of the Human Proeome Organization initiative towards comprehensive mapping of the brain, liver, and plasma proteomes. Particular emphasis is given to the limitations of the underlying data analysis techniques for large-scale collaborative proteomics. Nevertheless, the striking shortfall in new protein diagnostics emerging from proteomics research reflects a lack of critical capacity in biomarker verification. Recent advances made in the use of imaging-based biomarkers for detecting disease, determining disease severity, monitoring progression, and treatment efficacy have the potential to expedite the discovery, development, and delivery of new, affordable drugs and therapeutic agents. The premise of today’s drug development is that the mechanism of a disease is highly dependent upon underlying signaling and cellular pathways. Such pathways are often composed of complexes of physically interacting genes, proteins, or biochemical activities coordinated by metabolic intermediates, ions, and other small solutes and are investigated with molecular biology approaches. Nevertheless, recent declines in the pharmaceutical industry’s revenues indicate such approaches alone may not be adequate in creating successful new drugs. In BComputational Systems Bioinformatics and Bioimaging

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for Pathway Analysis and Drug Screening,[ Zhou and Wong point out the systematic combination of methods of genomics, proteomics, and metabonomics with highthroughput bioimaging has the potential to provide powerful means to better understand molecular interactions and pathways that lead to disease and generate new insights and indications for drug targets. The former methods provide the profiles of genes, proteins, and metabolites, whereas the latter techniques generate objective, quantitative phenotypes correlating to the molecular profiles and interactions. The paper authored by Pham, BFuzzy Fractal Analysis of Molecular Imaging Data,[ proposes the use of fractals as a computational tool for analyzing molecular imaging data that appear to be useful sources of information for understanding the interactions and behaviors of complex biological networks and the development of predictive medicine. Certain fractal characteristics of fluorescent microscope images of peroxisomes are studied, and the conceptual frameworks of fuzzy mixture fractal dimensions and fractal distortion measures are proposed for bioimage classification. Previously, the validation of cellular image analysis results has been a task of expert biologists, who manually analyzed the images and provided the ground truth to which the proposed analysis results have been compared. Such a traditional validation approach is unfeasible in the era of high-throughput biology. Lehmussola et al. propose in BSynthetic Images of High-Throughput Microscopy for Validation of Image Analysis Methods[ a computational framework for simulating fluorescence microscopy images of cell populations. The simulation framework allows generation of synthetic images with realistic characteristics and provides a potential platform in the validation and performance analysis of computational algorithms. The eighth to eleventh papers of this Special Issue discuss different

kinds of network inference. One of major challenges for postgenomic biology is to understand how genes, proteins, and small molecules dynamically interact to form molecular networks that facilitate sophisticated biological functions. In BModeling and Analyzing Biological Oscillations in Molecular Networks,[ Wang et al. deal with the recent developments on modeling molecular networks and analyzing synchronization of biooscillators in multicellular systems. They describe a general multicellular system with the consideration of external fluctuations and intercellular coupling to study the cooperative behaviors for a population of biooscillators. On the other hand, Gunawardena argues in BSignals and Systems: Towards a Systems Biology of Signal Transduction[ how signal transductionVthe process by which cells sense and respond to external signalsVcan be reconsidered from a systems perspective. His findings rely heavily on ideas and concepts from physical sciences coupled to new experimental strategies. Negative feedback loops are a common cellular motif underlying many important gene regulation pathways. Wagner and Stolovitzky present in BStability and Time-Delay Modeling of Negative Feedback Loops[ how they can analyze the linear stability of negative feedback loops with and without explicit time delays. When the degradation rates of each loop component are identical, an analytical solution of the condition of marginal stability is obtained. Generally, when the degradation rates of each component differ, a novel expansion of the condition of marginal stability in terms of the geometric mean and variance of the degradation rates is also obtained. These results are applied to elucidate the stability structure of the p53-Mdm2 feedback loop. Genome-scale metabolic networks of organisms are normally very large and complex. In BCentrality, Network Capacity, and Modularity as Parameters to Analyze the Core-Periphery Structure in Metabolic Networks,[

Silva et al. propose a core-periphery modular structure for metabolic networks in the literature. A parameter called Bcore coefficient[ is used to quantitatively evaluate the coreperiphery structure of metabolic networks, which is based on the concept of closeness centrality of metabolites and a newly defined parameter: network capacity. Furthermore, they developed a method to decompose metabolic networks based on a quantitative parameter of modularity and a procedure of core. The method has been done with genome-scale metabolic networks of five representative organisms, which include Aeropyrum pernix, Bacillus subtilis, Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens. Understanding the lymph node (LN) requires a dynamic integration of the massive volumes of data generated by extensive experimentations. This aspect of computational systems biology is exemplified by another paper, BThe Lymph Node B Cell Immune Response: Dynamic Analysis In-Silico[ by Swerdlin et al. Lymph nodes are organs in which lymphocytes respond to antigens to generate, among other cell types, plasma cells that secrete specific antibodies and memory lymphocytes for enhanced future responses to the antigen. In order to study these issues, the authors present a fully executable, bottom-up computerized model of the LN using the visual language of statecharts and the technology of reactive animation to create a dynamic front-end. They studied the effect of the amount of antigen and LN size on the emergent properties of lymphocyte dynamics, differentiation, and anatomic localization. Computational systems biology is an evolving field of interdisciplinary research. Due to page limitations, this Special Issue does not include a number of other emerging areas such as single nucleotide polymorphism array, promoter array, tissue array, and protein array, which quickly become common tools for in-vitro studies. On the other hand, advancement

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in molecular imaging and nanomedicine at tissue and organ levels opens up the opportunities to translate in-vitro findings into clinical space for in-vivo biomarker validation, early diagnosis, and therapy monitoring in patients. We expect that eventually the advancement made in computational systems biology would enable us to make sense of vast amounts of

nonlinear, heterogeneous, and multiscale biological information at the molecular, cellular, tissue, and organ levels and to decipher complex mechanisms of diseases and aid in therapeutic strategies. We hope this Special Issue would serve a catalyst to motivate research of computational systems biology for engineering communities who are looking for chal-

lenging and rewarding life-science problems to solve. h

REFERENCES

[3] L. Chong and L. B. Ray, BIntroduction to special issue on holistic biology,[ Science, vol. 295, pp. 1661, 2002. [4] X. Zhou and S. T. C. Wong, Computational Systems BioinformaticsVMethods and Biomedical Applications. Singapore: World Scientific, Jan. 2008.

[5] J. Chen, E. Dougherty, S. S. Demir, C. Friedman, C. S. Li, and S. T. C. Wong, BGrand challenges for multimodal bio-medical systems,[ IEEE Circuits Syst. Mag., vol. 5, no. 2, pp. 46Y52, 2005.

H. Kitano, BSystems biology: A brief overview,[ Science, vol. 295, no. 5560, pp. 1662Y1664, 2002. [2] H. Kitano, BComputational systems biology,[ Nature, vol. 420, no. 6912, pp. 206Y210, 2002.

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Acknowledgment The author would like to acknowledge the dedicated efforts of the staff of this PROCEEDINGS for their help and support throughout the preparation of this special issue.

ABOUT THE GUEST EDITORS Stephen T. C. Wong received B.S. (honors) degrees in electrical engineering from The University of Western Australia and the Ph.D. degree in computer science from Lehigh University, PA. He is a licensed professional engineer (PE). He received executive education from the Sloan School of Management, Massachusetts Institute of Technology, Cambridge; the School of Business, Stanford University, Stanford, CA, and the School of Business, Columbia University, New York. He is Vice Chair and Chief of Medical Physics, Department of Radiology, The Methodist Hospital, Houston, TX; Director, The Center for Biotechnology and Informatics, The Methodist Hospital Research Institute; and Professor of Radiology, Weill Cornell Medical College. He is Vice Chair of Radiology with The Methodist Hospital Research Institute and Cornell University, Houston, TX. He was Director of the Center for Bioinformatics, Harvard Center of Neurodegeneration and Repair, Boston, MA. He was Director of the Functional and Molecular Imaging Center and an Associate Professor of Radiology, Harvard Medical School and Brigham and Women_s Hospital, Boston. His research theme has been focused on the application of advanced technology to pragmatic biomedical problems and is based on the belief that problems of importance involve the interplay between theory and application. He is a hybrid scientist. He has published more than 270 peer-reviewed papers and has received seven patents in biotechnology and imaging. He also served on National Institutes of Health and National Science Foundation scientific review panels. He has broad R&D experience worldwide for two decades with Hewlett-Packard, Bell Labs, ICOT-Japanese 5th Generation Computer Systems project, Philips Electronics, Charles Schwab, and the University of California, San Francisco (UCSF). His earlier research involves pioneering work in optical time-domain reflectometer and optical networks, thinkjet (first inkjet) automation, 1 MB DRAM, and very large-scale integration factory automation before moving into the fields of bioinformatics and medical imaging. He was a key member of the UCSF picture archiving and communication system effort, founded product development departments of Philips Medical Systems, and directed the Web trading development and rearchitecturing of Schwab.com, one of the largest secured e-commerce sites.

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Maciej Ogorzallek (Fellow, IEEE) is Professor and Head of Department of Information Technologies ´ w (the oldest at the Jagiellonian University in Krako university in Poland funded in 1364). He has been also professor at the Department of Electrical Engineering, AGH University of Science and Tech´ w, Poland and holds the position of nology, Krako Chair Professor of Bio-signals and Systems at Hong Kong Polytechnic University (under the Distinguished Scholars Scheme). His research and teaching interests include circuit theory with an emphasis on Nonlinear and dynamic circuits, complex phenomena and chaos, neural networks, nonlinear signal analysis and processing, nonlinear methods for mixed signal circuit design, biomedical signal analysis and modeling. Author or co-author of over 270 technical publicationsVamong these are: one book (Chaos and Complexity in Nonlinear Electronic CircuitsV World Scientific) and 21 book chapters. He presented numerous keynote and plenary talks at conferences world-wide. He held several visiting positions: Swiss Federal Institute of Technology Lausanne, The Technical University of Denmark, Articial Brain Systems LaboratoryVInstitute of Physical and Chemical Research (RIKEN), Wako-shi, Japan; Electronics Research Laboratory, University of California, Berkeley. Centro Nacional de Microelectronica, Sevilla, Spain (Award from Spanish Ministry of Education), Kyoto University, Japan (Senior JSPS Award) and Goethe University Frankfurt-am-Main, Germany and Hong Kong Polytechnic University. He is a member of The Association of Polish Electrical Engineers, Polish Society of Theoretical and Applied Electrical Sciences, member of the Committee on Electrical Engineering, Computer Science and Automatic Control of the Polish Academy of Sciences, Krakow Section, member of the Section of Electronic Signal and Systems, Committee on Electronics and Telecommunication of the Polish Academy of Sciences. He is member of the Council of the European Circuits Society and was president of ECS 2003Y2005. Awards: IEEE Fellow (1997), Recipient of the IEEE CAS Golden Anniversary Medal 2000, IEEE Guillemin-Cauer (Best Paper) Award 2002. Distinguished Lecturer of the CAS Society 2001Y2003. Premio Ano Sabatico

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from the Spanish Ministry of Education; Senior AwardVJapan Society for Promotion of Science; Award of the Minister of National Education of Poland; National Education MedalVPoland; Silver Cross of MeritVPoland. He was Associate Editor for IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS Part I 1993Y1995 and 1999Y2001, Since 2004 member of the Editorial Board of the PROCEEDINGS OF THE IEEE. Between 2004 and 2007 he was the Editor-in-Chief of the Circuits and Systems Magazine. Associate Editor Int. J. Circuit Theory and Applications (1999Y ) Associate EditorVJournal of The Franklin Institute (1997Y ), Member of the Editorial Board for the Quarterly of Electrical Engineering and Electronics (Poland) (1993Y ), Member of the Editorial Board of Automatics (in Polish Automatyka). Member of the Editorial board of the International Journal of Bifurcation and Chaos. He was the Vice-chairman of the IEEE CAS Chapter Poland, recipient of the Chapter of the Year Award 1995, Chairman of the Technical Committee of Nonlinear Circuits and Systems of CAS Society 1997/1998, Chairman of the Organizing Committee 1994 Workshop on Nonlinear Dynamics of Electronic Systems, member of technical committees of several IEEE sponsored conferences, Special Sessions chairman for ISCAS_2000. Founding member of the CASS Technical Committee on Biomedical Circuits and Systems. General Chairman European Conference on Circuit Theory and Design 2003. He has been elected CAS Society Vice-President for Region 8 for 2002Y2004 and CAS Society Administrative Vice-president since 2004. Currently (2008) he serves as the President of Circuits and Systems Society IEEE.

Joseph Chang received the B.Eng. Degree in electrical and computer systems engineering from Monash University, and the Ph.D. degree from the Department of Otolaryngology, Faculty of Medicine, University of Melbourne, Australia. In Melbourne, he was a member of the Melbourne Cochlear Implant Program and headed the hearing aid program there. He is presently with the Nanyang Technological University, Singapore, and he was previously the Associate Dean of Research and Graduate Studies of the College of Engineering. He is also an adjunct professor at Texas A&M University, College Station. His research pertains to multidisciplinary biomedical and electronics including auditory prosthesis and devices. He has published over 100 peer reviewed papers and conference proceedings, and received a best paper award in the Microelectronics Conference. Dr. Chang served as panel member of the Thematic Strategic Research Programme, Science and Engineering Research Council, Agency for Science Technology and Research. He is an associate editor of the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS-I and -II and serves as a co-editor of the IEEE Circuits and Systems Magazine, BOpen Column.[ He was the General Chair of the 11th International Symposium on Integrated Circuits, Systems and Devices (2004), and co-chaired the recent IEEE-NIH Biomedical Information Science and Technology Initiative Workshop (2007). He has founded two high technology spin-off companies in medical/electroacoustics, and actively works in industrial research programs. He has been awarded numerous research grants, and presently holds 7 patents with several more pending.

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