Chen / Wang / Li Modeling Biomolecular Networks in Cells

Structures and Dynamics
1. Auflage 2010
ISBN: 978-1-84996-214-8
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 343 Seiten

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.

Autoren/Hrsg.

Chen, Luonan

Wang, Ruiqi

Li, Chunguang

Aihara, Kazuyuki

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;1 Introduction;12
3.1;1.1 Biological Processes and Networks in Cellular Systems;13
3.1.1;1.1.1 Gene Regulation: Gene Regulatory Networks;14
3.1.2;1.1.2 Signal Transduction: Signal Transduction Networks;17
3.1.3;1.1.3 Protein Interactions: Protein Interaction Networks;19
3.1.4;1.1.4 Metabolism: Metabolic Networks;19
3.1.5;1.1.5 Cell Cycles and Cellular Rhythms: Nonlinear Network Dynamics;22
3.2;1.2 A Primer to Networks;24
3.2.1;1.2.1 Basic Concepts of Networks;25
3.2.2;1.2.2 Topological Properties of Networks;26
3.3;1.3 A Primer to Dynamics;28
3.3.1;1.3.1 Dynamics and Collective Behavior;28
3.3.2;1.3.2 System States;29
3.3.3;1.3.3 Structures and Functions;29
3.3.4;1.3.4 Cellular Noise;31
3.3.5;1.3.5 Time Delays;31
3.3.6;1.3.6 Multiple Time Scales;32
3.3.7;1.3.7 Robustness and Sensitivity;33
3.4;1.4 Network Systems Biology and Synthetic Systems Biology;34
3.5;1.5 Outline of the Book;35
4;2 Dynamical Representations of Molecular Networks;42
4.1;2.1 Biochemical Reactions;42
4.2;2.2 Molecular Networks;49
4.3;2.3 Graphical Representation;49
4.3.1;2.3.1 Example of Interaction Graphs;50
4.3.2;2.3.2 Example of Incidence Graphs;53
4.3.3;2.3.3 Example of Species-reaction Graphs;53
4.4;2.4 Biochemical Kinetics;54
4.5;2.5 Stochastic Representation;55
4.5.1;2.5.1 Master Equations for a General Molecular Network;56
4.5.2;2.5.2 Stochastic Simulation;62
4.5.3;2.5.3 Analysis of Sensitivity and Robustness of Master Equations;67
4.5.4;2.5.4 Langevin Equations;68
4.5.5;2.5.5 Fokker–Planck Equations;73
4.5.6;2.5.6 Cumulant Equations;76
4.6;2.6 Deterministic Representation;79
4.6.1;2.6.1 Basic Kinetics;79
4.6.2;2.6.2 Deterministic Representation of a General Molecular System;81
4.6.3;2.6.3 Michaelis–Menten and Hill Equations;82
4.6.4;2.6.4 Total Quasi-steady-state Approximation;86
4.6.5;2.6.5 Deriving Rate Equations;88
4.6.6;2.6.6 Modeling Transcription and Translation Processes;90
4.7;2.7 Hybrid Representation and Reducing Molecular Networks;93
4.7.1;2.7.1 Decomposition of Biomolecular Networks;93
4.7.2;2.7.2 Approximation of Continuous Variables in Molecular Networks;97
4.7.3;2.7.3 Gaussian Approximation in Molecular Networks;98
4.7.4;2.7.4 Deterministic Approximation in Molecular Networks;100
4.7.5;2.7.5 Prefactor Approximation of Deterministic Representation;102
4.7.6;2.7.6 Stochastic Simulation of Hybrid Systems;105
4.8;2.8 Stochastic versus Deterministic Representation;109
5;3 Deterministic Structures of Biomolecular Networks;112
5.1;3.1 A General Structure of Molecular Networks;114
5.1.1;3.1.1 Basic Definitions;115
5.1.2;3.1.2 A General Structure for Gene Regulatory Networks;118
5.2;3.2 Gene Regulatory Networks with Cell Cycles;120
5.2.1;3.2.1 Gene Regulatory Networks for Eukaryotes;123
5.2.2;3.2.2 Gene Regulatory Networks for Prokaryotes;125
5.3;3.3 Interaction Graphs and Logic Gates;129
5.3.1;3.3.1 Interaction Graphs and Types of Interactions;129
5.3.2;3.3.2 Logic Gates;132
6;4 Qualitative Analysis of Deterministic Dynamical Networks;135
6.1;4.1 Stability Analysis;135
6.2;4.2 Bifurcation Analysis;139
6.3;4.3 Examples for Analyzing Stability and Bifurcations;142
6.3.1;4.3.1 A Simplified Gene Network;142
6.3.2;4.3.2 A Two-gene Network;145
6.3.3;4.3.3 A Three-gene Network;149
6.4;4.4 Robustness and Sensitivity Analysis;151
6.4.1;4.4.1 Robustness Measures;152
6.4.2;4.4.2 Sensitivity Analysis;153
6.5;4.5 Control Analysis;155
6.5.1;4.5.1 Control Coefficients of Metabolic Systems;155
6.5.2;4.5.2 Metabolic Control Theorems;157
6.6;4.6 Monotone Dynamical Systems;158
6.6.1;4.6.1 Notation;158
6.6.2;4.6.2 Decomposition of Monotone Systems;161
7;5 Stability Analysis of Genetic Networks in Lur’e Form;169
7.1;5.1 A Genetic Network Model;169
7.2;5.2 Stability Analysis of Genetic Networks Without Noise;172
7.3;5.3 Stochastic Stability of Gene Regulatory Networks;175
7.3.1;5.3.1 Mean-square Stability;175
7.3.2;5.3.2 Stochastic Stability with Disturbance Attenuation;179
7.4;5.4 Examples;184
8;6 Design of Synthetic Switching Networks;188
8.1;6.1 Types of Switches;190
8.2;6.2 Simple Switching Networks;194
8.2.1;6.2.1 Bistability in a Single Gene Network;194
8.2.2;6.2.2 The Toggle Switch;196
8.2.3;6.2.3 The MAPK Cascade Model;197
8.3;6.3 Design of Switching Networks with Positive Loops;199
8.4;6.4 Detection of Multistability;210
8.5;6.5 Enzyme-driven Switching Networks;217
9;7 Design of Synthetic Oscillating Networks;226
9.1;7.1 Simple Oscillatory Networks;227
9.1.1;7.1.1 Delayed Autoinhibition Networks;228
9.1.2;7.1.2 Goldbeter’s Models;231
9.1.3;7.1.3 Relaxation Oscillators;235
9.1.4;7.1.4 Stochastic Oscillators;239
9.2;7.2 Design of Oscillating Networks with Negative Loops;241
9.2.1;7.2.1 Theoretical Model of Cyclic Feedback Networks;242
9.2.2;7.2.2 A Special Cyclic Feedback Network;244
9.2.3;7.2.3 A General Cyclic Feedback Network;251
9.3;7.3 Construction of Oscillators by Non-monotone Dynamical Systems;253
9.4;7.4 Design of Molecular Oscillators with Hybrid Networks: General Formalism;264
10;8 Multicellular Networks and Synchronization;275
10.1;8.1 A General Multicellular Network for Deterministic Models;276
10.2;8.2 Deterministic Synchronization of Cellular Oscillators;279
10.2.1;8.2.1 Complete Synchronization;279
10.2.2;8.2.2 Other Types of Synchronization;282
10.3;8.3 Spontaneous Synchronization of Deterministic Models;283
10.4;8.4 Entrained Synchronization for Deterministic Models;287
10.5;8.5 Noise-driven Synchronization for Stochastic Models Without Coupling;291
10.6;8.6 A General Multicellular Network for Stochastic Models with Coupling;294
10.6.1;8.6.1 A Model;294
10.6.2;8.6.2 Example of a Gene Regulatory Network;296
10.6.3;8.6.3 Theoretical Analysis;302
10.6.4;8.6.4 Algorithm for Stochastic Simulation;307
10.6.5;8.6.5 Numerical Simulation;308
10.7;8.7 Deterministic Synchronization of Genetic Networks in Lur’e Form;311
10.8;8.8 Stochastic Synchronization of Genetic Networks in Lur’e Form;319
10.9;8.9 Transient Resetting for Synchronization Without Coupling;325
11;References;333
12;Index;346