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Modeling Biomolecular Networks in Cells Structures and Dynamics

:	Luonan Chen, Ruiqi Wang, Chunguang Li, Kazuyuki Aihara
:	Modeling Biomolecular Networks in Cells Structures and Dynamics
:	Springer-Verlag
:	9781849962148
:	1
:	CHF 85.90
:

:	Medizin
:	English

:	343
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Modeling Biomolecular Networks in Cells shows how the interaction between the molecular components of basic living organisms can be modelled mathematically and the models used to create artificial biological entities within cells. Such forward engineering is a difficult task but the nonlinear dynamical methods espoused in this book simplify the biology so that it can be successfully understood and the synthesis of simple biological oscillators and rhythm-generators made feasible. Such simple units can then be co-ordinated using intercellular signal biomolecules. The formation of such man-made multicellular networks with a view to the production of biosensors, logic gates, new forms of integrated circuitry based on 'gene-chips' and even biological computers is an important step in the design of faster and more flexible 'electronics'. The book also provides theoretical frameworks and tools with which to analyze the nonlinear dynamical phenomena which arise from the connection of building units in a biomolecular network.

Luonan Chen received his M.E. and Ph.D. degrees in electrical engineering from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was a member of the faculty of Osaka Sangyo University, Osaka, Japan, and then became a full Professor in the Department of Electrical Engineering and Electronics. He was also the founding director of Institute of Systems Biology, Shanghai University. Since 2010, he has been a professor at Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. His fields of interest are systems biology, bioinformatics, and nonlinear dynamics. He serves as associate editor or editorial board member for many systems biology related journals, e.g. BMC Systems Biology, IEEE/ACM Trans. on Computational Biology and Bioinformatics, IET Systems Biology, Mathematical Biosciences, International Journal of Systems and Synthetic Biology, and the Journal of Systems Science and Complexity. He also serves as Chair of Technical Committee of Systems Biology at the IEEE SMC Society. Ruiqi Wang received an M.S. degree in mathematics from Yunnan University, Kunming, China, in 1999, and a Ph. D. degree in mathematics from the Academy of Mathematics and Systems Science, CAS, Beijing, China, in 2002. Since 2007, he has been a member of the faculty of Shanghai University, Shanghai, China, where he is currently an Associate Professor at Institute of Systems Biology. His fields of interest are systems biology and nonlinear dynamics. Chunguang Li received an M.S. degree in Pattern Recognition and Intelligent Systems and a Ph.D. degree in Circuits and Systems from the University of Electronic Science and Technology of China, Chengdu, China, in 2002 and 2004, respectively. Currently, he is a Professor with the Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China. His current research interests include computational neuroscience, statistical signal processing, and machine intelligence. Kazuyuki Aihara received a B.E. degree of electrical engineering in 1977 and a Ph.D. degree of electronic engineering 1982 from the University of Tokyo, Japan. Currently, he is Professor of the Institute of Industrial Science, Professor of the Graduate School of Information Science and Technology, and Director of Collaborative Research Center for Innovative Mathematical Modelling at the University of Tokyo. His research interests include mathematical modeling of complex systems, parallel distributed processing with spatio-temporal chaos, and time series analysis of complex data.

	Preface	6
	Contents	8
	1 Introduction	12
	1.1 Biological Processes and Networks in Cellular Systems	13
	1.1.1 Gene Regulation: Gene Regulatory Networks	14
	1.1.2 Signal Transduction: Signal Transduction Networks	17
	1.1.3 Protein Interactions: Protein Interaction Networks	19
	1.1.4 Metabolism: Metabolic Networks	19
	1.1.5 Cell Cycles and Cellular Rhythms: Nonlinear Network Dynamics	22
	1.2 A Primer to Networks	24
	1.2.1 Basic Concepts of Networks	25
	1.2.2 Topological Properties of Networks	26
	1.3 A Primer to Dynamics	28
	1.3.1 Dynamics and Collective Behavior	28
	1.3.2 System States	29
	1.3.3 Structures and Functions	29
	1.3.4 Cellular Noise	31
	1.3.5 Time Delays	31
	1.3.6 Multiple Time Scales	32
	1.3.7 Robustness and Sensitivity	33
	1.4 Network Systems Biology and Synthetic Systems Biology	34
	1.5 Outline of the Book	35
	2 Dynamical Representations of Molecular Networks	42
	2.1 Biochemical Reactions	42
	2.2 Molecular Networks	49
	2.3 Graphical Representation	49
	2.3.1 Example of Interaction Graphs	50
	2.3.2 Example of Incidence Graphs	53
	2.3.3 Example of Species-reaction Graphs	53
	2.4 Biochemical Kinetics	54
	2.5 Stochastic Representation	55
	2.5.1 Master Equations for a General Molecular Network	56
	2.5.2 Stochastic Simulation	62
	2.5.3 Analysis of Sensitivity and Robustness of Master Equations	67
	2.5.4 Langevin Equations	68
	2.5.5 Fokker–Planck Equations	73
	2.5.6 Cumulant Equations	76
	2.6 Deterministic Representation	79
	2.6.1 Basic Kinetics	79
	2.6.2 Deterministic Representation of a General Molecular System	81
	2.6.3 Michaelis–Menten and Hill Equations	82
	2.6.4 Total Quasi-steady-state Approximation	86
	2.6.5 Deriving Rate Equations	88
	2.6.6 Modeling Transcription and Translation Processes	90
	2.7 Hybrid Representation and Reducing Molecular Networks	93
	2.7.1 Decomposition of Biomolecular Networks	93
	2.7.2 Approximation of Continuous Variables in Molecular Networks	97
	2.7.3 Gaussian Approximation in Molecular Networks	98
	2.7.4 Deterministic Approximation in Molecular Networks	100
	2.7.5 Prefactor Approximation of Deterministic Representation	102
	2.7.6 Stochastic Simulation of Hybrid Systems	105
	2.8 Stochastic versus Deterministic Representation	109
	3 Deterministic Structures of Biomolecular Networks	112
	3.1 A General Structure of Molecular Networks	114
	3.1.1 Basic Definitions	115
	3.1.2 A General Structure for Gene Regulatory Networks	118
	3.2 Gene Regulatory Networks with Cell Cycles	120
	3.2.1 Gene Regulatory Networks for Eukaryotes	123
	3.2.2 Gene Regulatory Networks for Prokaryotes	125
	3.3 Interaction Graphs and Logic Gates	129
	3.3.1 Interaction Graphs and Types of Interactions	129
	3.3.2 Logic Gates	132
	4 Qualitative Analysis of Deterministic Dynamical Networks	135
	4.1 Stability Analysis	135
	4.2 Bifurcation Analysis	139
	4.3 Examples for Analyzing Stability and Bifurcations	142
	4.3.1 A Simplified Gene Network	142
	4.3.2 A Two-gene Network	145
	4.3.3 A Three-gene Network	149
	4.4 Robustness and Sensitivity Analysis	151
	4.4.1 Robustness Measures	152
	4.4.2 Sensitivity Analysis	1