Systems Evolutionary Biology : Biological Network Evolution Theory, Stochastic Evolutionary Game Strategies, and Applications to Systems Synthetic Biology

__Systems Evolutionary Biology: Biological Network Evolution Theory, Stochastic Evolutionary Game Strategies, and Applications to Systems Synthetic Biology__ discusses the evolutionary game theory and strategies of nonlinear stochastic biological networks under random genetic variations and environmental disturbances and their application to systematic synthetic biology design. The book provides more realistic stochastic biological system models to mimic the real biological systems in evolutionary process and then introduces network evolvability, stochastic evolutionary game theory and strategy based on nonlinear stochastic networks in evolution. Readers will find remarkable, revolutionary information on genetic evolutionary biology that be applied to economics, engineering and bioscience. Front Cover Systems Evolutionary Biology: Biological Network Evolution Theory, Stochastic Evolutionary Game Strategies, and Applications... Copyright Dedication Contents Preface Part I: General Theory of Stochastic Evolutionary Biological Network Chapter 1: Introduction to Systems Evolutionary Biology 1.1. Introduction to Evolutionary Biology 1.2. Review of Current Systems Biology and Evolutionary Theory 1.3. Systems Evolutionary Biology as a Powerful Combination of Evolutionary Genetics With Systems Biology 1.4. The Scope of the Book Chapter 2: Stochastic Dynamics Systems and Stochastic Nash Game in Evolutionary Biological Networks 2.1. Introduction to the Robust Stability of Stochastic Dynamic Systems 2.2. Evolutionary Computation Algorithms Genetic Algorithm Canonical GA Evolutionary Algorithm Simple EA 2.3. Stochastic Nash Evolutionary Game in Stochastic Biological Systems 2.4. Conclusion 2.5. Appendix Appendix A: Proof of Proposition 2.5 Appendix B: Proof of Proposition 2.6 Chapter 3: Evolutionary Gene Regulatory Networks and Biochemical Networks 3.1. Introduction to Evolutionary Biological Systems 3.2. On the Interplay Between the Evolvability and Network Robustness of the Linear Stochastic Gene Regulatory Network Network Robustness of Linear Gene Regulatory Networks in Evolution Evolvability of a Linear Gene Regulatory Network in Evolution Tradeoff Between Environmental Robustness to Respond to Environmental Stimuli and Genetic Robustness to Tolerate Parametric ... 3.3. On the Interplay Between the Network Evolvability and Network Robustness of a Nonlinear Stochastic Gene Regulatory N ... A Network Robustness of a Nonlinear Gene Regulatory Network in Evolution Evolvability of a Nonlinear Gene Regulatory Network in Evolution Tradeoff Between the Environmental Robustness and Genetic Robustness of a Gene Regulatory Network in the Evolutionary Process 3.4. On the Interplay Between the Network Evolvability and Robustness of Biochemical Networks in Evolution 3.5. On the Interplay Between the Evolvability and Network Robustness of High-Level Biological Networks in Evolution 3.6. Discussion and Conclusion 3.7. Appendix Appendix A: Proof of Proposition 3.1 Appendix B: Proof of Proposition 3.2 Appendix C: Proof of Proposition 3.3 Appendix D: Proof of Proposition 3.4 Appendix E: Proof of Proposition 3.5 Appendix F: Proof of Proposition 3.6 Appendix G: Proof of Proposition 3.7 Appendix H: Proof of Proposition 3.8 Appendix I: Proof of Proposition 3.9 Chapter 4: Evolutionary Ecological Networks 4.1. Introduction to Intrinsic Robustness, Environmental Robustness, and Network Robustness in the Evolution of the Ecolo ... 4.2. Global Linearization and Finite Difference Methods for the Evolutionary Ecological Network 4.3. Computer Simulation Example 4.4. Conclusion 4.5. Appendix Appendix A: Proof of Proposition 4.1 Appendix B: Proof of Proposition 4.2 Part II: Applications of Network Evolution to Systems Synthetic Biology Chapter 5: Robust Design for Evolutionary Synthetic Gene Networks Under Genetic Mutations and Environmental Disturbances: ... 5.1. Introduction 5.2. Tradeoff Between Genetic Robustness, Environmental Robustness, and Network Robustness in Synthetic Biology 5.3. Robust Synthetic Gene Network Design via Network Evolution Through a GA Algorithm Network EA in Genotype space for Robust Synthetic Gene Network (see Fig. 5.2) 5.4. Robust Synthetic Gene Network Design via Library-Based Network Evolution Through a GA Searching Algorithm 5.5. Computer Simulation Example 5.6. Conclusion 5.7. Appendix Appendix A: Proof of Proposition 5.1 Appendix B: Proof of Proposition 5.2 Chapter 6: Robust Design of Genetic Networks: Evolutionary Systems Biology Approach via an Evolutionary Algorithm (EA) in ... 6.1. Stochastic Model for Biological Systems in vivo Under Intrinsic Genetic Mutation and External Noise Constitutive Transcription Enzymatic Transformation Regulated Transcription Translation 6.2. Robust Design of a Biological Circuit via Evolutionary Systems Biology Through the EA Searching Algorithm Initialization Fitness Reproduction Crossover Mutation 6.3. Design Example In Silico Robust Biological AND Gate Design Robust Repressilator Design 6.4. Discussion and Conclusion Chapter 7: On the Adaptive Design Rules of Biochemical Networks in Evolution 7.1. Introduction of Adaptive Evolution of Biochemical Networks 7.2. Mathematical Rules for Natural Selection in Biochemical Network Evolution Notations Model of a Biochemical Network Natural Selection Criteria for Biochemical Networks in Evolution 7.3. Computational Examples Diversity of the Biochemical Network Within Organisms or Individuals in Evolution 7.4. Conclusion Part III: Stochastic Evolutionary Game Strategies Chapter 8: Stochastic Nash Evolutionary Game as a Natural Selection Strategy in a Population of Biological Networks 8.1. Introduction to Biological Network Robustness and Evolvability 8.2. Stochastic Evolutionary Game in a Linear Biological Network 8.3. Stochastic Game in the Nonlinear Biological Network 8.4. Global Linearization Approach to the Nonlinear Stochastic Evolutionary Game 8.5. Computer Simulation Example 8.6. Conclusion 8.7. Appendix Appendix A: Proof of Proposition 8.1 Appendix B: Proof of Proposition 8.2 Appendix C: Proof of Proposition 8.3 Chapter 9: Stochastic Noncooperative and Cooperative Evolutionary Game Strategies of a Population of Biological Networks ... 9.1. Review of Evolutionary Game Strategies of Stochastic Biological Networks 9.2. Noncooperative Evolutionary Game Strategy of Stochastic Biological Networks Under Natural Selection Noncooperative Evolution Game in Linear Stochastic Biological Network Noncooperative Evolutionary Game in a Nonlinear Stochastic Biological Network Global Linearization Approach to the Nonlinear Stochastic Noncooperative Evolutionary Game 9.3. Cooperative Evolutionary Game Strategy of Stochastic Biological Networks Under Natural Selection Cooperative Evolutionary Game Strategy in the Linear Stochastic Biological Network Cooperative Evolutionary Game in a Nonlinear Stochastic Biological Network Global Linearization Approach to a Cooperative Evolutionary Game in the Nonlinear Stochastic Network 9.4. Simulation Examples Noncooperative Evolutionary Game Case Cooperative Evolutionary Game Case 9.5. Discussions and Conclusions 9.6. Appendix Appendix A: Proof of Proposition 9.1 Appendix B: Proof of Proposition 9.2 Appendix C: Proof of Proposition 9.3 Chapter 10: Evolutionary Game Strategy of an Evolutionary Biological Network of Somatic Cells in the Organ Carcinogenesis ... 10.1. Introduction to an Evolutionary Somatic Cells Network in the Organ Carcinogenesis and Aging 10.2. Stochastic Evolutionary Biological Network of an Organ in Carcinogenesis 10.3. Natural Selection in Carcinogenesis and Aging Minimax Nash Game Strategy of the Linear Evolutionary Biological Network in Carcinogenesis Minimax Game Strategy of the Nonlinear Biological Network in Carcinogenesis The Stochastic Evolutionary Game of the Nonlinear Cancer-Associated Network in Carcinogenesis 10.4. In Silico Example 10.5. Discussion 10.6. Conclusion 10.7. Appendix Appendix A: Proof of Proposition 10.1 Appendix B: Proof of Proposition 10.2 Appendix C: Proof of Proposition 10.3 Appendix D: Parameters Ai of the Global Linearization Scheme In Silico Example Part IV: Evolution Measurements of Biological Networks Chapter 11: On the System Entropy of Nonlinear Stochastic Biological Networks and Its Relationship to Network Evolution 11.1. Introduction to System Entropy and Network Evolution of Biological Networks 11.2. Measuring the System Entropy of Biological Networks On the Measurement of System Entropy in Linear Stochastic Biological Networks On the Measurement of System Entropy in the Nonlinear Stochastic Biological Network The Calculation of System Entropy in Nonlinear Stochastic Biological Networks by the Global Linearization Method 11.3. Example of Calculating System Entropy of Biological Networks 11.4. Conclusion 11.5. Appendix Appendix A: Proof of Proposition 11.1 Appendix B: Proof of Proposition 11.2 Appendix C: Proof of Proposition 11.3 Appendix D: Proof of Proposition 11.4 Appendix E: Proof of Proposition 11.5 Appendix F: Proof of Proposition 11.6 Appendix G Appendix H Chapter 12: On the Evolution Measurement of Somatic Networks by the Changes of Their Robustness and Response Ability in t ... 12.1. Introduction to the Evolutionary Gene Regulatory Network (GRN) in the Aging Process 12.2. Measuring Network Evolution in the Aging Process by the Systems Biological Method via Microarray Data Data Selection and Process Construction of a Dynamic Model for Aging-Related GRNs by Time-Profile Microarray Data Parameter Estimation of the GRN by Microarray Data Evolution of Network Robustness of Aging-Related GRNs From Young to Aged and CR Stages Under Intrinsic Perturbations due to ... Comparison of Network Response Abilities to External Signals Among Aging-Related Gene Networks at the Young and Aged Stages ... 12.3. Measurements of Network Evolution and Discussion of Evolutionary Network Robustness and Response Ability in the Agi ... Construction of Multiple Regulatory Loops of Evolutionary Gene Networks Associated With Aging-Related Pathophysiological Ph ... On the Evolution Measurement of Network Robustness and Response Ability of GRN at the Young, Aged, and CR Stages in the Agi ... Tradeoff Between Evolutionary Network Robustness and Network Response Ability During the Aging Process The Evolution Measurement of the Gene Response Ability of Individual Evolutionary Genes in the Aging-Related Regulatory Net ... Comparison of Individual Gene Response Abilities for Evolutionary FOXOs Among the Young, Aged, and CR Groups in the Aging P ... Individual Gene Response Ability of Evolutionary NF-κB in the Young, Aged, and CR Groups During the Aging Process Comparison of Individual Gene Response Abilities of the Evolutionary p53 Gene Among the Young, Aged, and CR Groups in the A ... 12.4. Conclusion 12.5. Appendix Appendix A:. Proof of Proposition 12.1 Appendix B:. Proof of Proposition 12.2 Chapter 13: Evolution of Network Biomarkers Measured by Microarray Data From Early to Late Stage Bladder Cancer Samples 13.1. Introduction to Network Biomarkers of Cancer 13.2. Materials and Evolution Measurement Methods of Network Biomarkers Overview of the Bladder Cancer Network Markers Construction Process Data Selection and Preprocessing Selection of Protein Pool and Identification of the Protein-Protein Interaction Networks (PPINs) for Cancerous and Noncance ... Determination of Significant Proteins and Their Network Structures in the Carcinogenesis of Four Types of Cancers Pathway Analysis of Evolutionary Network Biomarkers The Contribution of the Protein Interaction Network Will Affect the Results of Biomarkers and the Evolution of Network Biom ... 13.3. Results and Discussion on Evolutionary Network Biomarkers Time Evolution of the Network Biomarker From Early to Late Stage Bladder Cancer Evolutionary Network Marker of Early and Late Stage Bladder Cancer Pathway Analysis of Early Stage Bladder Cancer Pathway Analysis of Late Stage Bladder Cancer Pathway Analysis of Both Early and Late Stage Bladder Cancer 13.4. Conclusions 13.5. Appendix Appendix A: Parameter Identification of the Regression Model in Eq. (13.1) by the Maximum Likelihood Method Appendix B: Determination of Significant Protein Associations by AIC and Student's t-Test References Index Back Cover Systems Evolutionary Biology: Biological Network Evolution Theory, Stochastic Evolutionary Game Strategies, and Applications to Systems Synthetic Biology discusses the evolutionary game theory and strategies of nonlinear stochastic biological networks under random genetic variations and environmental disturbances and their application to systematic synthetic biology design. The book provides more realistic stochastic biological system models to mimic the real biological systems in evolutionary process and then introduces network evolvability, stochastic evolutionary game theory and strategy based on nonlinear stochastic networks in evolution. Readers will find remarkable, revolutionary information on genetic evolutionary biology that be applied to economics, engineering and bioscience. Explains network fitness, network evolvability and network robustness of biological networks from the systematic perspective Discusses the evolutionary noncooperative and cooperative game strategies of biological networks Offers detailed diagrams to help readers understand biological networks, their systematic behaviors and the simulational results of evolutionary biological networks Includes examples in every chapter with computational simulation to illustrate the solution procedure of evolutionary theory, strategy and results "[D]iscusses the evolutionary game theory and strategies of nonlinear stochastic biological networks under random genetic variations and environmental disturbances and their application to systematic synthetic biology design. The book provides more realistic stochastic biological system models to mimic the real biological systems in the evolutionary process and then introduces network evolvability, stochastic evolutionary game theory, and strategy based on nonlinear stochastic networks in evolution. These results are not only remarkable but also revolutionary in genetic evolutionary biology; they can also be applied to economics, engineering, and bioscience. Explains network fitness, network evolvability, and network robustness of biological networks from the systematic perspective. Discusses the evolutionary noncooperative and cooperative game strategies of biological networks. Offers detailed diagrams to help readers understand the biological networks, their systematic behaviors, and simulational results of the evolutionary biological network. Provides every chapter with at least one example with a computational simulation to illustrate the solution procedure of evolutionary theory and strategy and their results to confirm the proposed evolutionary theories and strategies"--Back cover