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

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(Tsing Hua Distinguished Chair Professor, Department of Electrical Engineering, National Tsing Hua University, Taiwan)
  • Formatas: PDF+DRM
  • Išleidimo metai: 03-Feb-2018
  • Leidėjas: Academic Press Inc
  • Kalba: eng
  • ISBN-13: 9780128140734
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Systems Evolutionary Biology: Biological Network Evolution Theory, Stochastic Evolutionary Game Strategies, and Applications to Systems Synthetic BiologyDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Išleidimo metai: 03-Feb-2018
  • Leidėjas: Academic Press Inc
  • Kalba: eng
  • ISBN-13: 9780128140734
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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
Preface xi
I General Theory Of Stochastic Evolutionary Biological Network
1 Introduction to Systems Evolutionary Biology
1.1 Introduction to Evolutionary Biology
3(1)
1.2 Review of Current Systems Biology and Evolutionary Theory
4(1)
1.3 Systems Evolutionary Biology as a Powerful Combination of Evolutionary Genetics With Systems Biology
5(1)
1.4 The Scope of the Book
6(5)
2 Stochastic Dynamics Systems and Stochastic Nash Game in Evolutionary Biological Networks
2.1 Introduction to the Robust Stability of Stochastic Dynamic Systems
11(4)
2.2 Evolutionary Computation Algorithms
15(4)
2.3 Stochastic Nash Evolutionary Game in Stochastic Biological Systems
19(3)
2.4 Conclusion
22(1)
2.5 Appendix
22(3)
3 Evolutionary Gene Regulatory Networks and Biochemical Networks
3.1 Introduction to Evolutionary Biological Systems
25(5)
3.2 On the Interplay Between the Evolvability and Network Robustness of the Linear Stochastic Gene Regulatory Network
30(8)
3.3 On the Interplay Between the Network Evolvability and Network Robustness of a Nonlinear Stochastic Gene Regulatory Network in Evolution
38(11)
3.4 On the Interplay Between the Network Evolvability and Robustness of Biochemical Networks in Evolution
49(4)
3.5 On the Interplay Between the Evolvability and Network Robustness of High-Level Biological Networks in Evolution
53(2)
3.6 Discussion and Conclusion
55(4)
3.7 Appendix
59(6)
4 Evolutionary Ecological Networks
4.1 Introduction to Intrinsic Robustness, Environmental Robustness, and Network Robustness in the Evolution of the Ecological System
65(5)
4.2 Global Linearization and Finite Difference Methods for the Evolutionary Ecological Network
70(5)
4.3 Computer Simulation Example
75(1)
4.4 Conclusion
76(1)
4.5 Appendix
76(5)
II Applications Of Network Evolution To Systems Synthetic Biology
5 Robust Design for Evolutionary Synthetic Gene Networks Under Genetic Mutations and Environmental Disturbances: Genetic Algorithm (GA) Approach in Genotype Space
5.1 Introduction
81(1)
5.2 Tradeoff Between Genetic Robustness, Environmental Robustness, and Network Robustness in Synthetic Biology
82(6)
5.3 Robust Synthetic Gene Network Design via Network Evolution Through a GA Algorithm
88(5)
5.4 Robust Synthetic Gene Network Design via Library-Based Network Evolution Through a GA Searching Algorithm
93(1)
5.5 Computer Simulation Example
94(4)
5.6 Conclusion
98(1)
5.7 Appendix
99(4)
6 Robust Design of Genetic Networks: Evolutionary Systems Biology Approach via an Evolutionary Algorithm (EA) in Phenotype Space
6.1 Stochastic Model for Biological Systems in vivo Under Intrinsic Genetic Mutation and External Noise
103(4)
6.2 Robust Design of a Biological Circuit via Evolutionary Systems Biology Through the EA Searching Algorithm
107(5)
6.3 Design Example In Silico
112(6)
6.4 Discussion and Conclusion
118(5)
7 On the Adaptive Design Rules of Biochemical Networks in Evolution
7.1 Introduction of Adaptive Evolution of Biochemical Networks
123(2)
7.2 Mathematical Rules for Natural Selection in Biochemical Network Evolution
125(6)
7.3 Computational Examples
131(5)
7.4 Conclusion
136(5)
III Stochastic Evolutionary Game Strategies
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
141(3)
8.2 Stochastic Evolutionary Game in a Linear Biological Network
144(10)
8.3 Stochastic Game in the Nonlinear Biological Network
154(4)
8.4 Global Linearization Approach to the Nonlinear Stochastic Evolutionary Game
158(5)
8.5 Computer Simulation Example
163(4)
8.6 Conclusion
167(1)
8.7 Appendix
168(5)
9 Stochastic Noncooperative and Cooperative Evolutionary Game Strategies of a Population of Biological Networks Under Natural Selection
9.1 Review of Evolutionary Game Strategies of Stochastic Biological Networks
173(5)
9.2 Noncooperative Evolutionary Game Strategy of Stochastic Biological Networks Under Natural Selection
178(15)
9.3 Cooperative Evolutionary Game Strategy of Stochastic Biological Networks Under Natural Selection
193(9)
9.4 Simulation Examples
202(6)
9.5 Discussions and Conclusions
208(2)
9.6 Appendix
210(5)
10 Evolutionary Game Strategy of an Evolutionary Biological Network of Somatic Cells in the Organ Carcinogenesis and Aging Process
10.1 Introduction to an Evolutionary Somatic Cells Network in the Organ Carcinogenesis and Aging
215(4)
10.2 Stochastic Evolutionary Biological Network of an Organ in Carcinogenesis
219(4)
10.3 Natural Selection in Carcinogenesis and Aging
223(15)
10.4 In Silico Example
238(2)
10.5 Discussion
240(4)
10.6 Conclusion
244(1)
10.7 Appendix
245(8)
IV Evolution Measurements Of Biological Networks
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
253(2)
11.2 Measuring the System Entropy of Biological Networks
255(15)
11.3 Example of Calculating System Entropy of Biological Networks
270(5)
11.4 Conclusion
275(1)
11.5 Appendix
276(9)
12 On the Evolution Measurement of Somatic Networks by the Changes of Their Robustness and Response Ability in the Aging Process via Microarray Data
12.1 Introduction to the Evolutionary Gene Regulatory Network (GRN) in the Aging Process
285(3)
12.2 Measuring Network Evolution in the Aging Process by the Systems Biological Method via Microarray Data
288(9)
12.3 Measurements of Network Evolution and Discussion of Evolutionary Network Robustness and Response Ability in the Aging Process
297(10)
12.4 Conclusion
307(1)
12.5 Appendix
308(3)
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
311(2)
13.2 Materials and Evolution Measurement Methods of Network Biomarkers
313(9)
13.3 Results and Discussion on Evolutionary Network Biomarkers
322(17)
13.4 Conclusions
339(1)
13.5 Appendix
340(7)
References 347(20)
Index 367
Bor-Sen Chen received B.S. degree of electrical Engineering from Tatung Institute of Technology in 1970, M.S. degree of Geophysics from National Central University in 1973, and PhD in Electrical Engineering from University of Southern California in 1982. He is an expert on the topic of nonlinear robust control and filter designs based on stochastic Nash game theory to override the influence of intrinsic random fluctuations and attenuate the effect of environmental disturbances, which can be applied to evolutionary game strategies of biological networks under natural selection to respond to random genetic variations and environmental disturbances in the evolutionary process. Prof. Chen had audited more than 10 courses of biology before his research in systems biology. He has published about 100 papers in bioinformatics and systems biology. Further, he have published more than 100 papers in system theory and control, and more than 80 papers of signal processing and communication. In the last three years, he has also published 7 monographs. He was elected to an IEEE Fellow in 2001 and became an IEEE Life Fellow in 2014.
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