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About this book
About this book
With extraordinary clarity, 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.
CHAPTER 1 Systems Biology: Elements and Basic Concepts 21. Introduction 71.1. General Systems Theory 81.2. Principles of clear thinking 92. The Concept of Truth in non-deductive Science. 102.1. Grand Theories of Truth 102.2. Deduction, Induction, and Pragmatic Inference 113. Reductionism vs. holism 144. The Art of Modeling 154.1. Models of Convoluted (Complex) Systems 164.1.1. The meaning of the word model 164.1.2. The Modeling Relationship 174.1.3. Cascades of Models 174.2. Metaphors in Systems Biology 175. The Legacy and the Future of Systems Biology 17References 17CHAPTER 2UNTERSTANDING THROUGH MODELING: A Historical Perspective and Review of Biochemical Systems Theory as a Powerful Tool for Systems Biology 18Abstract 201. Introduction 201.1. Historical Background 211.2. Beyond Reductionism 251.3. Challenges 271.4. Reconstruction 301.5. Goals of systems biology 321.6. Modeling Approaches 342. Biochemical Systems Theory 382.1. Representation of Reaction Networks 392.2. Rate Laws 412.2.1. Mass Action Kinetics. 422.2.2. Michaelis-Menten Rate Law 432.2.3. Power-Law Rate Laws. 442.3. Solutions to the System of Equations 462.3.1. Numerical Integration. 462.3.2. Linearization 462.3.3. Power-Law Approximation. 472.4. Nonlinear Canonical Models in BST 482.4.1. Generalized Mass Action System. 482.4.2. S-systems. 493. Working with Models Described by GMA and S-systems 513.1. From Biochemical Maps to Systems of Equations 513.1.1. Map-drawing rules. 523.1.2. Maps to GMA Systems 533.1.3. Maps to S-systems 533.1.4. GMA Systems to S-systems 543.2. Steady-State Solutions for S-systems 553.3. Stability 563.4. Steady-State Sensitivity Analysis 593.5. Precursor-Product Constraints 613.6. Moiety Conservation Constraints 633.7. System Dynamics 643.7.1. Solving the System 643.7.2. Visualization of time courses 653.7.3. Visualization of dynamics in the phase plane 653.8. Parameter Estimation 673.8.1. From rate laws to power laws 673.8.2. Parameter estimation from time course data 684. Applications of Biochemical Systems Theory 684.1. Modeling and Systems Analysis 684.2. Controlled Comparisons of Biochemical Systems 694.3. System Optimization 735. Metabolic Control Analysis 745.1. Relationship between BST and MCA 766. Future 766.1. Model Extensions and Needs 766.2. Computational Support 796.3. Applications 807. Conclusion 81Acknowledgments 82References 82CHAPTER 3 Thermostatics: A poster child of systems thinking 911. Basic Concepts 922. The Zeroth Law 933. The first law 944. The Second Law 945. Standard States and Tables 976. States versus processes 977. Reformulations 998. Implications for living systems 1009. The analogy between Shannon "inform