Systems Biology: Principles, Methods, and Concepts, 1st Edition (Hardback) book cover

Systems Biology

Principles, Methods, and Concepts, 1st Edition

Edited by A.K. Konopka

CRC Press

256 pages | 4 Color Illus. | 42 B/W Illus.

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With extraordinary clarity,the Systems Biology: Principles, Methods, and Concepts focuses on the technical practical aspects of modeling complex or organic general systems. It also provides in-depth coverage of modeling biochemical, thermodynamic, engineering, and ecological systems. Among other methods and concepts based in logic, computer science, and dynamical systems, it explores pragmatic techniques of General Systems Theory. This text presents biology as an autonomous science from the perspective of fundamental modeling techniques. A complete resource for anyone interested in biology as an exact science, it includes a comprehensive survey, review, and critique of concepts and methods in Systems Biology.

Reviews

“This book focuses on the technical aspects of modeling complex or organic general systems. Designed as a desk reference, this volume serves the needs of practitioners of diverse fields of life science, as well as those of intellectually mature individuals who would themselves like to practice the art of systems biology in the future. It also provides in-depth coverage of modeling biochemical, thermodynamic, engineering, and ecological systems. … This text presents biology as an autonomous science from the perspective of fundamental modeling techniques and serves as a complete resource for anyone interested in biology as an exact science.”

— In Anticancer Research, Vol. 27, No. 3B, May/June 2007

Table of Contents

CHAPTER 1    Systems Biology: Elements and Basic Concepts   2

1. Introduction    7

1.1. General Systems Theory      8

1.2. Principles of clear thinking    9

2. The Concept of Truth in non-deductive Science.            10

2.1. Grand Theories of Truth        10

2.2. Deduction, Induction, and Pragmatic Inference           11

3. Reductionism vs. holism         14

4. The Art of Modeling    15

4.1. Models of Convoluted (Complex) Systems     16

4.1.1. The meaning of the word model      16

4.1.2. The Modeling Relationship 17

4.1.3. Cascades of Models         17

4.2. Metaphors in Systems Biology         17

5. The Legacy and the Future of Systems Biology            17

References        17

CHAPTER 2

UNTERSTANDING THROUGH MODELING: A Historical Perspective and Review of Biochemical Systems Theory as a Powerful Tool for Systems Biology        18

Abstract            20

1. Introduction    20

1.1. Historical Background          21

1.2. Beyond Reductionism          25

1.3. Challenges  27

1.4. Reconstruction        30

1.5. Goals of systems biology     32

1.6. Modeling Approaches          34

2. Biochemical Systems Theory  38

2.1. Representation of Reaction Networks            39

2.2. Rate Laws  41

2.2.1. Mass Action Kinetics.       42

2.2.2. Michaelis-Menten Rate Law           43

2.2.3. Power-Law Rate Laws.      44

2.3. Solutions to the System of Equations            46

2.3.1. Numerical Integration.       46

2.3.2. Linearization         46

2.3.3. Power-Law Approximation.            47

2.4. Nonlinear Canonical Models in BST   48

2.4.1. Generalized Mass Action System. 48

2.4.2. S-systems.          49

3. Working with Models Described by GMA and S-systems           51

3.1. From Biochemical Maps to Systems of Equations      51

3.1.1. Map-drawing rules.            52

3.1.2. Maps to GMA Systems     53

3.1.3. Maps to S-systems          53

3.1.4. GMA Systems to S-systems         54

3.2. Steady-State Solutions for S-systems           55

3.3. Stability      56

3.4. Steady-State Sensitivity Analysis     59

3.5. Precursor-Product Constraints          61

3.6. Moiety Conservation Constraints       63

3.7. System Dynamics   64

3.7.1. Solving the System           64

3.7.2. Visualization of time courses         65

3.7.3. Visualization of dynamics in the phase plane          65

3.8. Parameter Estimation          67

3.8.1. From rate laws to power laws         67

3.8.2. Parameter estimation from time course data           68

4. Applications of Biochemical Systems Theory    68

4.1. Modeling and Systems Analysis       68

4.2. Controlled Comparisons of Biochemical Systems       69

4.3. System Optimization           73

5. Metabolic Control Analysis      74

5.1. Relationship between BST and MCA 76

6. Future           76

6.1. Model Extensions and Needs           76

6.2. Computational Support         79

6.3. Applications            80

7. Conclusion    81

Acknowledgments          82

References        82

CHAPTER 3   Thermostatics: A poster child of systems thinking   91

1. Basic Concepts         92

2. The Zeroth Law          93

3. The first law   94

4. The Second Law        94

5. Standard States and Tables    97

6. States versus processes         97

7. Reformulations           99

8. Implications for living systems 100

9. The analogy between Shannon "information" and thermostatic entropy    101

10. Finite-Time Thermodynamics 101

References        103

CHAPTER 4  Friesian Epistemology        105

Bibliography:     114

CHAPTER 5  Reconsidering the Notion of the Organic       115

Abstract            115

1. Introduction    117

2. Chance and Propensities        119

3. The Origins of Organic Agency            122

4. The Integrity of Organic Systems         124

5. Formalizing Organic Dynamics            125

6. Under Occam's Razor 128

7. The Organic Perspective         130

Acknowledgements        132

References        132

CHAPTER 6   The Metaphor of Chaos ………………………………………………..134

1.   Theories of chaotic behavior  134

1.1.  Chaos and General Systems Theory            135

1.2. Deterministic Chaos            138

2.   Nonlinear Dynamics -  Chaos in Work            144

2. 1.   Energetic and Informational Interactions      145

2. 2.   Open  and Closed  Systems - from  a Single Molecule to Metaman  147

3.   Chaos and Fractal Geometry of Nature           148

3.1.   Fractal dimension  149

3.2.  Natural Fractals      151

4.   Chaos and Fractals in Modeling of  Natural Phenomena           153

5.   Examples  of  Order,  Chaos,  and  Randomness  in  Natural Processes           157

6.   Examples of Systems and Processes that are not Easily Modeled with Chaos  159

7.   Conclusions and Open Problems       161

References        163

CHAPTER 7   Biological complexity: An engineering perspective    167

1. When engineering decisions involve living processes     168

2. The role of causation  169

2.1 Characterization by causation            169

2.2 Classes of causation            173

2.3 Why machines do what they do         174

2.4 Why efficient cause is what it is         179

2.5 Entailment of downward causation     183

3. Do hierarchical loops of entailment make sense?          185

3.1 Impredicatives: Answering Russell's Paradox  186

3.2 Function: Answering Aristotle's objection        191

4. Why organisms are not machines        192

4.1 Both the loop and the hierarchy are crucial     192

4.2 Ambiguity is incomputable    195

4.3 Can function in context be characterized by differential equations?        198

5. Why not reductionism?           199

5.1 What is reductionism?          200

5.2 Where does reductionism fall short?   202

5.3 The measurement problem    203

5.4 Brain physiology       205

5.5 Other bizarre effects 207

5.5.1 Determinism versus freewill 207

5.5.2 Non-locality           209

5.5.3 Language  210

6. Does the endogenous paradigm ignore past insights?    212

6.1 Computational theory of mind            213

6.2 Artificial intelligence  214

6.2.1 Connectionism      215

6.6.2 Markov chains       216

6.2.3 Genetic Algorithms            216

6.2.4 Fuzzy systems     217

6.3 The self-replicating automaton           218

7. Incomputable does not mean non-engineerable?           221

Acknowledgements        223

Bibliography      223

CHAPTER 8   

The von Neumann's Self-Replicator and a Critique of its Misconceptions  …………..232

1.Introduction     232

2. The General and Logical Theory           234

2.1. The first question: Reliability from Unreliability            234

2.2. The second question: Self-replication            235

3. The Illinois Lectures    236

3.1. Computing Machines In General       236

3.2. Rigorous Theories of Control and Information  237

3.3. Statistical Theories of Information      239

3.4. The Role of High and of Extremely High Complication 241

4. Re-Evaluation of the Problems of Complicated Automata-Problems of Hierarchy and Evolution      244

5. The 29-state Automaton          250

5.1. Clearing up Some Confusion 250

5.2. Five Questions and Five Models        251

5.3. Implementation        253

6. Presuppositions and Insights   255

6.1. The Objective          255

6.2. Ambiguity   256

6.3. Impredicativity         258

6.4. Probability  259

6.5. Computers versus Brains     262

7. CONCLUSIONS         263

Acknowledgements        265

References        265

CHAPTER 9  The mathematical structure of thermodynamics        271

1. Introduction    271

2. A Historical Introduction to Thermodynamics    272

3. Definitions and Axioms           274

4. Thermodynamic States, Coordinates, and Manifolds      276

5. Manifolds and Differential Forms          278

6. Pfaffian Equations      280

7. Thermodynamics - The First Law         284

8. Thermodynamics - The Second Law     286

9. Riemannian Structure 288

10. Conclusions for Systems Biology       288

References        289

APPENDIX    Systems Biology: A Glossary of Terms        291

Subject Categories

BISAC Subject Codes/Headings:
SCI008000
SCIENCE / Life Sciences / Biology / General
SCI010000
SCIENCE / Biotechnology
SCI055000
SCIENCE / Physics