Principles, Methods, and Concepts
E-Book, Englisch, 256 Seiten
ISBN: 978-1-4200-1512-6
Verlag: Taylor & Francis
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Students and researchers in systems biology and bioinformatics.
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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
