Complex Systems Science in Biomedicine doc

Complex Systems Science
in Biomedicine
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Complex Systems Science in Biomedicine
Edited by Thomas S. Deisboeck and J. Yasha Kresh
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Complex Systems Science
in Biomedicine
Edited by
Thomas S. Deisboeck
Department of Radiology
Massachusetts General Hospital, and
Harvard Medical School
Boston, Massachusetts
and
J. Yasha Kresh
Department of Cardiothoracic Surgery and Medicine
Drexel University College of Medicine
Philadelphia, Pennsylvania
Thomas S. Deisboeck, M.D.
Assistant Professor of Radiology (HMS,
MGH, HST)
Director, Complex Biosystems Modeling
Laboratory
Harvard–MIT (HST) Athinoula A.
Martinos Center for Biomedical
Imaging
Massachusetts General Hospital–East
Bldg. 149, 13th Street, Charlestown,
MA 02129

J. Yasha Kresh, Ph.D., F.A.C.C.
Professor and Research Director
Dept. of Cardiothoracic Surgery
and
Professor of Medicine

Director, Cardiovascular Biophysics
Drexel Univ. College of Medicine
215 N. 15th Street, MS# 111
Philadelphia, PA 19102-1192

Front cover: The first figure appears courtesy of Gustavo Stolovitzky (IBM T. J. Watson Research Center).
The second appears courtesy of J. Yasha Kresh (Drexel University College of Medicine). The third appears
with permission from Nature and originally appeared in print as Figure 1 in Nature
41 1:41–42, 2001 ‘‘Lethality and centrality in protein networks,’’ by H. Jeong, S. P. Mason, A L. Barab´asi,
and Z. N. Oltvai. The fourth appears courtesy of Ricard V. Sol´e (ICREA Complex Systems Lab,
Universitat Pompeu Fabra). The right-hand figure appears courtesy of Josh Snyder, David Tuch, Nouchine
Hadjikhani, and Bruce Fischl (Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical
School).
Library of Congress Control Number: 2005934914
ISBN-10: 0-387-30241-7
ISBN-13: 978-0387-30241-6
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v
ACKNOWLEDGMENTS

We gratefully acknowledge the participation of everyone involved in the making
of this textbook. Our special thanks go to the contributing authors, whose
expertise and enthusiastic commitments made this volume a reality. We also
thank our colleagues, whose insights helped shape this book, in particular Tom
Kepler, Stuart Kauffman, Ary Goldberger, and Bernard Blickman, as well as
Yuri Mansury, Chaitanya Athale, Brian Gregor, Meg Etherington, and Pam
Fried. We especially appreciate the energy and excitement of the Springer
publishing team (Aaron Johnson, Tim Oliver, Jasmine Benzvi, Shoshana
Sternlicht, and Krista Zimmer), whose unwavering patience and tenacity
ensured that the project go the distance. Finally, our deepest thanks to our
families, who encouraged us with their love and support through the years of
continuous intensity and concentration that this effort required. We could not
have done it without you: Lizette M. Pérez-Deisboeck and Myrna P. Kresh.
Thomas S. Deisboeck, MD J. Yasha Kresh, PhD
Boston, Massachusetts Philadelphia, Pennsylvania
PREFACE
Work on Deisboeck and Kresh's Complex Systems Science in BioMedicine
started years ago. In fact, thoughts and ideas leading up to this textbook date
back to our first conversation, sometime in the fall of 1996. We quickly found
common ground, and talked about emergence and self-organization and their
relevance for medicine. We were both fascinated by the idea of complexity and
marveled about its tremendous possibilities for cancer research, which was then
and still is Tom's main scientific interest. Much has happened in science and
technology since we first discussed our vision. For instance, in a remarkable
international effort the human genome has been deciphered, nanotechnology has
become a household name, and computing infrastructure, a critical enabler, is as
powerful and affordable as ever before.
It is exactly because of this unprecedented progress that Complex Systems
Science in BioMedicine is now making a case for a new approach in the life
sciences. So let us start then with the obvious question first: why do we need a

new fresh approach to ensure continued progress in the biomedical sciences?
Did decades of methodically thorough research not yield great accomplishments
and trigger an unparalleled productivity, with each year seeing thousands of
scientific papers published in peer-reviewed journals? Certainly. Reductionism
has led to ever-growing knowledge about isolated molecular pathways and
selected portions of disease processes. We concede, dissecting biological
mechanisms into bits and pieces has been utterly successful—if the number of
fragmented discoveries is to be the decisive parameter. However, if we take
understanding connectivity across scales, or better yet, function as the yardstick
for measuring scientific achievements, much less progress can be claimed.
Neither the vision nor the technical tools necessary to achieve these goals are
"mainstream" yet. But there are signs in the biomedical sciences that things are
changing—clear signs.
Indeed, most of the field involved in mapping the human genome in the
1990s is now engaged in functional genomics. Beginning to realize that the sum
of its genes and proteins will not be able to explain a single cell's behavior,
much less cell–cell interaction dynamics, let alone entire organ systems, we
remember Aristotle, who had already argued that "The whole is more than the
sum of its parts." For biomedicine it means that, no matter how many more
vii
viii PREFACE
details we enthusiastically discover on ever smaller scales, we fail in deducing
the complexity of a cell or multicellular tissue on the basis of this fragmented
knowledge alone. In other words, piecing it together afterwards will not work.
We need a new scientific approach, one that takes the nonlinearity of the
majority of biological processes as much into account as their multi-scaled
character. We believe that we are at a crucial bifurcation, where we need to
integrate knowledge rather than dissect it, where we need to collaborate
intensely across disciplines, theoretically and experimentally, in order to move
forward. Complex systems science can match this challenge. Intrinsically

multidisciplinary, it comprises concepts and quantitative tools that enable us to
investigate how multiple biological elements interact and how molecular
networks guide cell behavior and ultimately determine tissue function.
You might wonder how this is any different from, say physiology, a
cornerstone of classic biomedical training. Indeed, physiology, the science of
how living organisms function, may well be regarded as a predecessor of what
many in the computational biology community now call "systems biology" and
which clearly overlaps with complexity science in its goals. Where they differ,
however, is in the approach to get there. Complex systems science applies a set
of concepts and quantitative tools that are based on analogy and commonality, if
not universality, between distinctively different systems, biologically or
otherwise. Let us give you an example. The reason my, i.e., Tom's, laboratory
developed an agent-based model to study cancer cell migration was an
admittedly rather tired look out of a window while approaching London's
Heathrow Airport by night several years back. What caught my attention was
that, from above, the busy suburbs and streets resembled the cellular clusters and
path patterns of a growing biosystem where single cells rather than people
represent the system's individual "agents." Could one possibly investigate the
metabolism-driven interaction of a rapidly evolving multicellular system,
internally and with its microenvironment, in a way similar to how social
scientists analyze the adaptive, economically driven behavior seen in expanding
human societies? If so, then why not try an urban-planning approach for cancer
research in an effort to better understand the dynamics of growth, migration and
aggregation in tumor cell populations? Chapter 6.3 (Part III) summarizes some
of the intriguing results arising from this line of work. This example illustrates
how complex systems science approaches the problem at hand with tools
adapted from nonlinear dynamics, applying sometimes rather abstract modeling
and simulation techniques ranging from network theory to agent-based
frameworks. It follows a "top–down" concept based on the claim that
abstraction, not simplification, is the key to understanding the complexity of

interaction between multiple parts on and across various scales of interest. That,
however, is distinctively different from classic physiology, which uses
biophysics and engineering concepts to describe the biological entity of interest
in as much detail as available and, thus, "bottom–up." Let us emphasize that
tackling the very same scientific problem from two seemingly opposing sides
should not be seen as much as a case of competing approaches but as an exciting
opportunity to exploit their mutual strengths in going forward.
PREFACE ix
Complex Systems Science in BioMedicine presents some of the fundamental
theoretical basics of this rapidly emerging field and exemplifies the potential of
the new approach by studying such diverse areas as molecular networks and
developmental processes, the immune and nervous systems, the heart, cancer,
and multi-organ failure. In this effort, the book itself follows a multi-scaled
approach from molecular to macroscopic, thereby discussing both the normal
and diseased states in selected topics. The invited contributions intentionally
represent the dynamic state of the field in that biophysics, bioengineering, and
computational biology modeling works are put side by side with complex
systems-driven approaches. We believe that such juxtaposition not only anchors
the new approach properly in established terrain but also helps showcase the
differences.
A section on emergent technologies, no matter how long, can hardly ever be
complete and, since the book was started years back, must run the risk of being
outdated by the time of publication. By taking this risk we show by example that
this novel approach has already led to and will continue to inspire design and
development of cutting edge technology, ranging from micro-fluidics and
innovative database management to multi-scale bioengineering, neuromorphic
systems, functional MR imaging, and even operating room design. Undoubtedly,
these and other techniques will feedback vital data and thus help complex
systems science achieve its goals.
Finally, is there something like complex systems science at all or is it

merely a powerful tool kit? As stated earlier and as reviewed in the book, there
are certain techniques that are ubiquitous for the study of complex systems in
economics, population dynamics, and biology. The title of the book reveals that
we advocate the application of these techniques also to relevant areas in
biomedicine where reductionism may have reached its limits. Nothing more,
nothing less. As such, this book presents visionary ideas and their potential
impact on future directions in biomedical research. It is not and cannot be
definitive. Rather, we let the reader judge how far this, our field, has come, and
if the presented work at this stage represents merely a promising, fresh approach
or if it already signals the dawn of a new and yet to be fully defined science.
As described in detail in Yasha Kresh's introductory chapter, the origins of
applying systems ideas in one form or another to the life sciences date back at
least several decades. And while initial efforts to move complex systems further
into the center of mainstream medicine were undertaken by a few pioneers, this
has certainly changed. Over the last years, many colleagues have embraced the
necessity of moving in this new direction, also documented by the enthusiastic
feedback we received when we asked for participation in this multi-authored
book. The newly established multidisciplinary graduate and postgraduate
training curricula, sprouting complex systems-related academic centers as well
as novel crosscutting grant funding programs, are testimony that these ideas are
starting to catch on. What counts now are the steps we take in order to further
foster this nascent development. As such, if Complex Systems Science in
BioMedicine can help draw more attention to the application of complexity
techniques to important questions in biomedicine and thus help support ongoing
x PREFACE
and upcoming scientific, teaching, and training efforts, we will consider it
successful.
The quest for novel ways of thinking was what brought us together back in
1996, first as colleagues, now also as friends. It is the immense potential of
complex systems science that provided a source of relentless energy for this

textbook and that continues to fuel our scientific work.
Thomas S. Deisboeck, MD Stuart A. Kauffman, MD
Boston, Massachusetts Santa Fe, New Mexico
2004
CONTENTS
Part I: Introduction
INTEGRATIVE SYSTEMS VIEW OF LIFE: PERSPECTIVES
FROM GENERAL SYSTEMS THINKING 3
J. Yasha Kresh
1. Introduction 4
2. General System Theory: The Laws of Integrated Wholes 5
3. Systemic Principles of Cybernetics 6
4. Biological Systematics: Understanding Whole Systems 9
5. Systems Biology and Mathematical Modeling 17
6. Emergence: Complex Adaptive Systems 21
7. The Complex Systems in Systems Biology 26
Part II: Complex Systems Science: The Basics
Chapter 1
METHODS AND TECHNIQUES OF COMPLEX SYSTEMS
SCIENCE: AN OVERVIEW 33
Cosma Rohilla Shalizi
1. Introduction 33
2. Statistical Learning and Data-Mining 37
3. Time-Series Analysis 46
4. Cellular Automata 63
5. Agent-Based Models 65
6. Evaluating Models of Complex Systems 70
7. Information Theory 76
8. Complexity Measures 81
9. Guide to Further Reading 95

Chapter 2
NONLINEAR DYNAMICAL SYSTEMS 115
Joshua E. S. Socolar
1. Introduction 115
2. Dynamical Systems in General 118
xi
xii CONTENTS
3. Linear Systems and Some Basic Vocabulary 119
4. Nonlinear Effects in Simple Systems 121
5. Two Types of Complexity: Spatial Structure and Network Structure 130
6. Discussion and Conclusions 136
Chapter 3
BIOLOGICAL SCALING AND PHYSIOLOGICAL TIME:
BIOMEDICAL APPLICATIONS 141
Van M. Savage and Geoffrey B. West, in collaboration with A.P. Allen,
J.H. Brown, B.J. Enquist, J.F. Gillooly, A.B. Herman, and W.H. Woodruff
1. Introduction 142
2. Model Description: Theory for the Origin of Scaling Relationships 146
3. Biomedical Applications 153
4. Discussion and Conclusions 158
Chapter 4
THE ARCHITECTURE OF BIOLOGICAL NETWORKS 165
Stefan Wuchty, Erszébet Ravasz, and Albert-László Barabási
1. Introduction 165
2. Basic Network Features 166
3. Networks Models 169
4. Biological Networks 172
5. Conclusions 176
Chapter 5
ROBUSTNESS IN BIOLOGICAL SYSTEMS:

A PROVISIONAL TAXONOMY 183
David C. Krakauer
1. A Fundamental Biological Dichotomy: Robustness and Evolvability 183
2. Genotypic versus Environmental versus Functional Robustness 185
3. Principles and Parameters of Robust Organization 185
4. Case Studies of Robust Principles 190
5. Awaiting a Synthesis of Robustness in Biological Systems 201
Part III: Complex Adaptive Biosystems: A Multi-Scaled Approach
Section III.1: Complexity in Molecular Networks
Chapter 1.1
NOISE IN GENE REGULATORY NETWORKS 211
Juan M. Pedraza and Alexander van Oudenaarden
1. Introduction 211
2. The Master Equation Approach 212
3. The Langevin Approach 220
4. Discussion and Conclusions 224
CONTENTS xiii
Chapter 1.2
MODELING RNA FOLDING 227
Ivo L. Hofacker and Peter F. Stadler
1. Introduction 227
2. RNA Secondary Structures and Their Prediction 230
3. Neutral Networks in the Sequence Space 232
4. Conserved RNA Structures 235
5. Discussion 236
Chapter 1.3
PROTEIN NETWORKS 247
Andreas Wagner
1. Introduction 247
2. Large-Scale Approaches to Identify Protein Expression 248

3. Identifying Protein Interactions 253
4. Medical Applications 259
Chapter 1.4
ELECTRONIC CELL ENVIRONMENTS: COMBINING GENE,
PROTEIN, AND METABOLIC NETWORKS 265
Pawan Dhar and Masaru Tomita
1. Introduction 265
2. Biomedical Background 266
3. Modeling and Simulation 268
4. Future Work and Its Relevance to Biomedicine 277
Section III.2: The Cell as a Complex System
Chapter 2.1
TENSEGRITY, DYNAMIC NETWORKS, AND COMPLEX
SYSTEMS BIOLOGY: EMERGENCE IN STRUCTURAL AND
INFORMATION NETWORKS WITHIN LIVING CELLS 283
Sui Huang, Cornel Sultan, and Donald E. Ingber
1. Introduction: Molecular Biology and Complex System Sciences 284
2. Complexity in Living Systems 287
3. Model: Networks as the General Conceptual Framework 288
4. Results 290
5. Conclusion 306
Chapter 2.2
SPATIOTEMPORAL DYNAMICS OF EUKARYOTIC
GRADIENT SENSING 311
K.K. Subramanian and Atul Narang
1. Introduction 312
2. Model and Simulation 317
3. Future Work 327
xiv CONTENTS
Chapter 2.3

PATTERNING BY EGF RECEPTOR: MODELS FROM
DROSOPHILA DEVELOPMENT 333
Lea A. Goentoro and Stanislav Y. Shvartsman
1. Introduction 333
2. Two Examples of EGFR Signaling in Fruit Fly Development 335
3. Modeling and Computational Analysis of Autocrine and Paracrine Networks 341
4. Conclusions and Outlook 349
Section III.3: Developmental Biology and the Cardiac System
Chapter 3.1
DEVELOPMENTAL BIOLOGY: BRANCHING
MORPHOGENESIS 357
Sharon R. Lubkin
1. Introduction 357
2. Previous Work 360
3. Model 361
4. Discussion and Conclusions 368
Chapter 3.2
MODELING CARDIAC FUNCTION 375
Raimond L. Winslow
1. Introduction 375
2. Cellular Models 376
3. Models of the Cardiac Ventricles 392
4. Discussion and Conclusions 402
Chapter 3.3
CARDIAC OSCILLATIONS AND ARRHYTHMIA ANALYSIS 409
Leon Glass
1. Introduction 409
2. Two Arrhythmias with a Simple Mathematical Analysis 412
3. Reentrant Arrhythmias 414
4. Future Prospects 416

Section III.4: The Immune System
Chapter 4.1
HOW DISTRIBUTED FEEDBACKS FROM MULTIPLE SENSORS
CAN IMPROVE SYSTEM PERFORMANCE: IMMUNOLOGY
AND MULTIPLE-ORGAN REGULATION 425
Lee A. Segel
1. Introduction 425
2. Therapy as an Information-Yielding Perturbation 426
3. Employing Information on Progress toward Multiple Goals to
Regulate the Immune Response 427
CONTENTS xv
4. Cytokines 431
5. Contending with Multiple Independent Goals 432
6. Relevance to Biomedicine 433
Appendix: Equations for the Mathematical model 435
Chapter 4.2
MICROSIMULATION OF INDUCIBLE REORGANIZATION
IN IMMUNITY 437
Thomas B. Kepler
1. Introduction 437
2. Model 440
3. Results 444
4. Discussion and Conclusion 447
Chapter 4.3
THE COMPLEXITY OF THE IMMUNE SYSTEM: SCALING LAWS 451
Alan S. Perelson, Jason G. Bragg, and Frederik W. Wiegel
1. Introduction 451
2. Scaling Laws in Immunology 453
3. Conclusions 457
Section III.5: The Nervous System

Chaper 5.1
NEUROBIOLOGY AND COMPLEX BIOSYSTEM MODELING 463
George N. Reeke Jr.
1. Neuronal Systems Dynamics 464
2. Future Work and Relevance to Biomedicine 473
3. Conclusions 477
Chapter 5.2
MODELING SPONTANEOUS EPISODIC ACTIVITY IN DEVELOPING
NEURONAL NETWORKS 483
Joël Tabak and John Rinzel
1. Introduction 484
2. Spontaneous Activity in Developing Networks 484
3. Model of Spontaneous Activity in the Embryonic Chick Spinal Cord 487
4. Properties and Applications of the Model 490
5. Discussion and Future Work 500
Chapter 5.3
CLINICAL NEURO-CYBERNETICS: MOTOR LEARNING
IN NEURONAL SYSTEMS 507
Florian P. Kolb and Dagmar Timmann
1. Introduction 507
2. Experimental Approaches and Behavioral Data 512
3. Theoretical Approaches 522
4. Relevance for Patients and Therapy 529
xvi CONTENTS
Section III.6: Cancer: A Systems Approach
Chapter 6.1
MODELING CANCER AS A COMPLEX ADAPTIVE SYSTEM:
GENETIC INSTABILITY AND EVOLUTION 537
Kenneth J. Pienta
1. Introduction 537

2. Cancer Risk in the Context of an Evolutionary Paradigm 538
3. Cancer Evolution in the Context of Recent Human Evolution 540
4. Modeling Cancer as a Complex Adaptive System at the
Level of the Cell 544
5. Conclusion: Applying Complexity Theory toward a
Cure for Cancer 551
Chapter 6.2
SPATIAL DYNAMICS IN CANCER 557
Ricard V. Solé, Isabel González García, and José Costa
1. Introduction 557
2. Population Dynamics 559
3. Competition in Tumor Cell Populations 560
4. Competition with Spatial Dynamics 563
5. Metapopulation Dynamics and Cancer Heterogeneity 565
6. Discussion 569
Chapter 6.3
MODELING TUMORS AS COMPLEX BIOSYSTEMS:
AN AGENT-BASED APPROACH 573
Yuri Mansury and Thomas S. Deisboeck
1. Introduction 573
2. Previous Works 576
3. Mathematical Model 579
4. Specifications of the Model 586
5. Basic Model Setup 589
6. Results 592
7. Discussion, Conclusions, and Future Work 597
Section III.7: The Interaction of Complex Biosystems
Chapter 7.1
THE COMPLEXITY OF DYNAMIC HOST NETWORKS 605
Steve W. Cole

1. Introduction 605
2. Model 606
3. Results 607
4. Discussion and Conclusions 621
Appendix 622
CONTENTS xvii
Chapter 7.2
PHYSIOLOGIC FAILURE: MULTIPLE ORGAN
DYSFUNCTION SYNDROME 631
Timothy G. Buchman
1. Introduction 631
2. Previous Work 633
3. Model 635
4. Results 636
5. Implications for Treatment 637
6. Summary and Perspective 638
Chapter 7.3
AGING AS A PROCESS OF COMPLEXITY LOSS 641
Lewis A. Lipsitz
1. Introduction 641
2. Measures of Complexity Loss 643
3. Examples of Complexity Loss with Aging 646
4. Mechanisms of Physiologic Complexity 648
5. Loss of Complexity as a Pathway to Frailty in Old Age 649
6. Interventions to Restore Complexity in Physiologic Systems 650
7. Conclusion 652
Part IV: Enabling Technologies
Chapter 1
BIOMEDICAL MICROFLUIDICS AND ELECTROKINETICS 657
Steve Wereley and Carl Meinhart

1. Introduction 658
2. DC Electrokinetics 659
3. AC Electrokinetics 663
4. Experimental Measurements of Electrokinetics 671
5. Conclusions 675
Chapter 2
GENE SELECTION STRATEGIES IN MICROARRAY EXPRESSION
DATA: APPLICATIONS TO CASE-CONTROL STUDIES 679
Gustavo A. Stolovitzky
1. Introduction 679
2. Previous Work: Gene Selection Methods in Microarray Data 681
3. Combining Selection Methods Produces a Richer Set of
Differentially Expressed Genes 685
4. Gene Expression Arrays Can Be Used for Diagnostics:
A Case Study 690
5. Discussion and Conclusions 695
xviii CONTENTS
Chapter 3
APPLICATION OF BIOMOLECULAR COMPUTING TO
MEDICAL SCIENCE: A BIOMOLECULAR DATABASE
SYSTEM FOR STORAGE, PROCESSING, AND RETRIEVAL
OF GENETIC INFORMATION AND MATERIAL 701
John H. Reif, Michael Hauser, Michael Pirrung, and Thomas LaBean
1. Introduction 702
2. Review of Biotechnologies for Genomics and the Biomolecular
Computing Field 706
3. A Biomolecular Database System 709
4. Applying Our Biomolecular Database System to Execute
Genomic Processing 725
5. Discussion and Conclusions 729

Chapter 4
TISSUE ENGINEERING: MULTISCALED REPRESENTATION
OF TISSUE ARCHITECTURE AND FUNCTION 737
Mohammad R. Kaazempur-Mofrad, Eli J. Weinberg,
Jeffrey T. Borenstein, and Joseph P. Vacanti
1. Introduction 737
2. Tissue-Engineering Investigations at Various Length Scales 741
3. Continuing Efforts in tissue Engineering 755
4. Conclusion 757
Chapter 5
IMAGING THE NEURAL SYSTEMS FOR MOTIVATED BEHAVIOR
AND THEIR DYSFUNCTION IN NEUROPSYCHIATRIC ILLNESS 763
Hans C. Breiter, Gregory P. Gasic, and Nikos Makris
1. Introduction 764
2. In Vivo Measurement of Human Brain Activity Using fMRI 766
3. Theoretical Model of Motivation Function 770
4. Neuroimaging of the General Reward/Aversion System Underlying
Motivated Behavior 776
5. Implications of Reward/Aversion Neuroimaging for Psychiatric Illness 787
6. Linking the Distributed Neural Groups Processing Reward/Aversion
Information to the Gene Networks that Establish and Modulate
Their Function 791
Chapter 6
A NEUROMORPHIC SYSTEM 811
David P. M. Northmore, John Moses, and John G. Elias
1. Introduction: Artificial Nervous Systems 811
2. The Neuron and the Neuromorph 812
3. Hardware System 814
4. Neuromorphs in a Winnerless Competition Network 816
5. Sensorimotor Development in a Neuromorphic Network 818

6. Simulated Network 819
7. Neuromorphs in Neural Prosthetics 824
8. Conclusions 824
CONTENTS xix
Chapter 7
A BIOLOGICALLY INSPIRED APPROACH TOWARD
AUTONOMOUS REAL-WORLD ROBOTS 827
Frank Kirchner and Dirk Spenneberg
1. Introduction 827
2. Mechatronics 828
3. Ambulation Control 830
4. Results 832
5. Discussion and Outlook 834
Chapter 8
VIRTUAL REALITY, INTRAOPERATIVE NAVIGATION,
AND TELEPRESENCE SURGERY 837
M. Peter Heilbrun
1. Introduction 838
2. Biomedical Background 838
3. The Future 843
4. Discussion and Conclusions 846
Index 849
Part I
I
NTRODUCTION
3
INTEGRATIVE SYSTEMS VIEW OF LIFE:
PERSPECTIVES FROM GENERAL
SYSTEMS THINKING
J. Yasha Kresh

Departments of Cardiothoracic Surgery and Medicine,
Drexel University College of Medicine, Philadelphia
The application of systems thinking and the principles of general systems science to
problems in the life sciences is not a new endeavor. In the 1960s systems theory and bi-
ology attracted the interest of many notable biologists, cyberneticists, mathematicians,
and engineers. The avalanche of new quantitative data (genome, proteome, physiome) in-
cited by the boundless advances in molecular and cellular biology has reawakened inter-
est in and kindled rediscovery of formal model-building techniques. The manifold
perspectives presented in many ways is a re-embodiment of the general theory of organ-
ismic systems and serves as an impetus to suggest that organized complexity can be un-
derstood. The particular affinity expressed in this essay is a reflection of how closely my
thinking is associated with the thoughts of Ludwig von Bertalanffy, Ervin Laszlo, and
Robert Rosen. We are, by all accounts, at the threshold of a postgenomic era that truly
belongs to the biology of systems.
Thus, the task is not so much to see what no one yet has seen,
but to think what nobody yet has thought about that which
everybody sees.
—Schopenhauer
Systems here systems there systems everywhere
Address correspondence to: J. Yasha Kresh, Departments of Cardiothoracic Surgery and Medi-
cine, Drexel University College of Medicine, 245 North 15th Street, MS#111, Philadelphia, PA
19102
-
1192 ().
4 J. Y. KRESH
1.
INTRODUCTION
The historical framework and ideas presented here feature the disciplines
that spawned the science of complex systems (e.g., self-organizing, autopoietic
networks, dissipative structures, chaos, fractals). In particular, we use general

systems theory (GST), control system theory (i.e., cybernetics, homeodynam-
ics), and dynamical systems theory (nonlinear, chaotic), the forerunners of crea-
tive systems thinking, to formulate a coherent theory and elucidate the essential
properties of biological phenomena such as structural and functional organiza-
tion, regulatory control mechanisms, and robustness and fragility.
The defining aims of systems thinking:
— The Believing: why do I see what I see?
— The Being: why do things stay the same?
— The Becoming: why do things change?
The notion of a system comprised of interdependent elements has been the
subject of human concern and inquiry for centuries. Man has explored the solar
system and the constellations since the beginning of recorded time. We, as a
species, have struggled with the complicated array of interconnected elements
that control our internal and external world. The more formal understanding of a
system, offered by systems science, as a complex of components and their inter-
actions has not changed dramatically through the years.
An inkling of systems science was anticipated by the Gestalten in physics, a
natural worldview proposed in the 1920s. According to the great leader in the
field of GST, Ludwig von Bertalanffy, the ideas of physical Gestalten were the
precursors intended to elaborate the most general properties of inorganic com-
pared with organic systems. It is worth mentioning that physicists study closed
systems, as compared with real systems, that communicate and exchange energy
(information) with the environment and thus self-organize, learn, and adapt. Of
particular note is the historical precedence that gave rise to the genesis of sys-
tems theory as a reaction to the confinement of reductionism and motivated by a
keen desire to reestablish the unity of science. Some aspects of intellectual tradi-
tion and scientific history are worthy of repetition.
Systems was and remains a fashionable catchword. In the introduction to his
seminal book, General System Theory (1), von Bertalanffy wrote in 1967 that
the concept of systems permeated all fields of science as well as popular think-

ing, jargon, and mass media. Common parlance continues to include concepts
such as adaptation, control, differentiation, dynamic behavior, hierarchy, robust-
ness, reliability, and sensitivity.
INTEGRATIVE SYSTEMS VIEW OF LIFE 5
The reader is encouraged to visit the Principia Cybernetica website (http://
pespmc1.vub.ac.be), an extensive condensed repository of historical and con-
temporary thinking addressing the age-old philosophical question—What is the
meaning of life?—by starting with a formal definition:
Systems Theory:
The transdisciplinary study of the abstract organiza-
tion of phenomena, independent of their substance,
type, or spatial or temporal scale of existence. It in-
vestigates both the principles common to all complex
entities, and the (usually mathematical) models that
can be used to describe them (2).
2.
GENERAL SYSTEM THEORY
:
THE LAWS OF
INTEGRATED WHOLES
Von Bertalanffy (1) developed the tenets of system theory in the late 1920s
(when he himself was in his twenties). He drew attention to a new perspective as
a method, which he called "organismic biology," that assigns a self-
organizational dynamics to biological systems. To this end he developed the
kinetic theory of open systems, characterized by equifinality and steady state.
His main goal was to unite metabolism, growth and morphogenesis, and sense
physiology into a dynamic theory of stationary open systems. He spoke of it as
an attempt at explanation, calling it "The System Theory of the Organism." It
was not until the late 1940s that he recognized that "there exist models, princi-
ples and laws that apply to generalized systems or their subclasses irrespective

of their particular kind, the nature of the component elements, and the relations
or ‘forces’ between them. We postulate a new discipline called General System
Theory." What sustains this systems view is the recognition that one cannot
compute the behavior of the whole from the behavior of its parts. More impor-
tantly, the preservation of the multitude of interacting atoms, molecules, cells,
tissues, and organs is valued by the complex of relationships that entail the or-
ganization and not by the individuality of their participation.
When we try to pick up anything by itself
we find it is attached to everything in the universe.
—John Muir
This grand unification concept was criticized as pseudoscience and said to
be an attempt to connect things holistically. Such criticisms would have dissi-
6 J. Y. KRESH
pated with the recognition that GST is merely a perspective or paradigm and that
such basic conceptual frameworks are central to the development of exact scien-
tific theory and a new way of doing science. GST was not meant to be a single
overarching theory (which history tells us has a short-lived existence). Above
all, it is a system-theory; it deals with systemic phenomena—organisms, groups,
and the like (e.g., nations, economies, biosphere, astronomical universe). It
views a system as an integrated whole of its subsidiary components, not a
mechanistic aggregate of parts in isolable causal relations (3).
Some of the concepts and principles are rigorous enough to be considered
laws in addition to providing a general framework for theory construction. "If
this be considered not enough, the reader would do well to remember that a true
general theory of all such varieties of systems would constitute a master science
that would make Einstein's attempt at a unified field theory pale by comparison"
(from Foreword by Ervin Laszlo for a collection of essays gathered together and
published in honor of von Bertalanffy two years after his death in 1972). As it
was then and remains now, the science of systems is not restricted to a particular
level of biological order or set of relationships. This perspective is all inclusive;

it allows us to look at a gene network or a cell as an integrated system or to look
at the organ, the organism, the family unit, the community, nation, and the bio-
sphere as an organized system (see Figure 1). The concept of a holon (from the
Greek holos = whole) is used to explain the unity of greater purpose. Arthur
Koestler popularized this term to describe the hybrid nature of subwholes/parts
in living systems (4). A natural byproduct of this view of a system is the holar-
chy that is formed in which systems are simultaneously self-contained wholes in
relation to their subordinated parts and dependent parts when viewed by the
overarching whole (Figure 2). The manifestation of a relationally distributed
control structure is the creation of autonomous, self-reliant functional modules
that can handle contingencies without central control or intervention.
3.
SYSTEMIC PRINCIPLES OF CYBERNETICS
Information is information not matter or energy. No material-
ism which does not admit this can survive at the present day.
—Norbert Weiner
A special branch of general systems theory that studies systems that can be
mapped using loops or looping structure became known as cybernetics. The
term cybernetics stems from the Greek kybernetes (meaning steersman, gover-
nor, or pilot as in autopilot). It became known as a theory of the communication
and control of regulatory feedback (information loop). The modern abstract
view of cybernetics encompasses the study of systems (subsystems) and their
INTEGRATIVE SYSTEMS VIEW OF LIFE 7
Figure 1. Holarchies and the order of nature: hierarchical structures/units of life leading to complexification
in organizational order. The notion of entities that are "independent wholes" and "dependent" parts seen as
an overarching assimilation of lower order "parts" into the adjoining level of "wholes." The part–whole
Holon dualism allows for concurrent upward–downward causality (arrows) to coexist (4). The overarching
levels of interconnected and interdependent continuum suggest an integrated worldview perspective and
thinking. The basic causal tension between parts (i.e., mechanistic, reductionist, atomistic) and whole (i.e.,
organistic, systemic, ecological) is depicted by arrows. (Artwork by M. Clemens.)