This volume presents a timely and comprehensive overview of biological networks at all organization levels in the spirit of the complex systems approach. It discusses the transversal issues and fundamental principles as well as the overall structure, dynamics, and modeling of a wide array of biological networks at the molecular, cellular, and population levels. Anchored in both empirical data and a strong theoretical background, the book therefore lends valuable credence to the complex systems approach.
Contents: Scale-Free Networks in Biology (E Almaas et al.); Modularity in Biological Networks (R V Solé et al.); Inference of Biological Regulatory Networks: Machine Learning Approaches (F d Alché-Buc); Transcriptional Networks (F Képès); Protein Interaction Networks (K Tan & T Ideker); Metabolic Networks (D A Fell); Heterogeneous Molecular Networks (V Schächter); Evolution of Regulatory Networks (A Veron et al.); Complexity in Neuronal Networks (Y Frégnac et al.); Networks of the Immune System (R E Callard & J Stark); A History of the Study of Ecological Networks (L-F Bersier); Dynamic Network Models of Ecological Diversity, Complexity, and Nonlinear Persistence (R J Williams & N D Martinez); Infection Transmission through Networks (J S Koopman).
Author(s): Frantois Kopos, Frantois Kopos
Series: Complex systems and interdisciplinary science 3
Edition: 1
Publisher: World Scientific
Year: 2007
Language: English
Pages: 531
City: [Hackensack], NJ
CONTENTS......Page 12
Challenges......Page 6
Outline......Page 8
Acknowledgements......Page 9
Contributors......Page 14
1. Introduction......Page 16
2.1. Degree Distribution......Page 17
2.2. Clustering Coefficient......Page 18
2.3. Subgraphs and Motifs......Page 19
3. Network Models......Page 23
3.2. Scale-Free Network Model......Page 24
3.3. Hierarchical Network Model......Page 25
3.4. Bose-Einstein Condensation and Networks......Page 26
4. Network Utilization......Page 27
4.1. Flux Utilization......Page 28
4.2. Gene Interactions......Page 30
5. Conclusion......Page 31
References......Page 32
1. Introduction......Page 36
2. Topological Overlap......Page 40
3. Modular Networks: The Role of Tinkering......Page 48
4. Conclusions......Page 51
References......Page 53
1. Introduction......Page 56
1.1. Feasibility of Inference......Page 57
1.2. Overview of Methods......Page 58
2.1. Gene Regulatory Networks......Page 60
2.2. Machine Learning: A Short Definition......Page 61
2.3. A Methodology for the Conception of a Learning Algorithm......Page 62
3.1. Prerequisites......Page 64
3.2. Questions When Accounting for Dynamics......Page 65
3.2.1. Encoding the Data......Page 66
3.2.2. Identifiability, Learnability and Sample Complexity......Page 67
3.2.3. Time-Scale, Sampling Frequency and Irregular Sampling......Page 68
3.3. Deterministic Models of Dynamics......Page 69
3.3.1. Temporal Boolean Network Models......Page 70
3.3.2. Linear Networks......Page 71
3.3.3. Artificial Recurrent Neural Networks......Page 72
3.4. Probabilistic Models of Dynamics......Page 73
3.4.1. Linear Models and Linear State-Space Models......Page 74
3.4.2. Dynamical Bayesian Networks Using non Parametric Regression for Conditional Probability Distributions (CPD)......Page 76
3.4.3. Models of Biochemical Processes......Page 77
3.5. Static Models of Causal Dependencies......Page 78
3.5.2. Probabilistic Relational Models......Page 79
3.5.4. Factor Graph Networks (FGN)......Page 80
4. Learning and Optimization......Page 81
4.1. Exact Learning and Best-Fit Approaches......Page 82
4.2.1. Mean Squared Error and Weight Decay for Neural Networks......Page 83
4.2.2. Maximum A Posteriori Approaches for Learning Parameters of Bayesian Networks......Page 84
4.2.3. Structure Learning......Page 86
5.1. Introduction to Validation......Page 87
5.2.1. Model Selection via Sampling and Re-sampling Methods......Page 89
5.2.3. Performance Evaluation on Known Networks (Simulated or Real)......Page 90
5.3. Biological Validation......Page 91
6. Conclusion and Perspectives......Page 92
References......Page 93
1. Introduction......Page 98
2.2. Regulatory Proteins or Dedicated Transcription Factors......Page 99
3. Mode of Interaction Between Transcription Factors and DNA Regulatory Regions......Page 100
3.2. Genetic Interaction Map......Page 101
4. Methodology......Page 103
4.1. Complementary DNA Microarrays......Page 104
4.2. Oligonucleotide Chips......Page 105
4.4. Reverse Transcription–Polymerase Chain Reaction (RT-PCR)......Page 107
4.6. Chromatin Immunoprecipitation......Page 108
4.7. Bioinformatics......Page 111
5. Computational Modeling......Page 112
5.1. Graphs and Their Derivatives......Page 113
5.2. Boolean Modeling......Page 114
5.3. Generalized Logical Modeling......Page 115
5.4. Petri Nets......Page 116
5.6. Ordinary Differential Equations......Page 117
5.7. Partial Differential Equations......Page 118
5.8. Stochastic Equations......Page 119
5.9. Modeling Strategy......Page 120
6.2. Analysis of the Global Topology......Page 121
6.3. A Case Study and Its Biological Interpretation......Page 122
7.1. Analysis of the Local Topology......Page 124
7.2. A Case Study of a Microorganism......Page 125
7.3. A Case Study of a Multicellular Organism......Page 126
7.4. Combinatorial Transcription Logic......Page 127
8.1.1. Interest of Modularity......Page 129
8.1.2. Implementing Modularity......Page 130
8.2.1. Feedback Circuits......Page 131
8.2.2. Regulatory Triangles (“Feedforward Loops”)......Page 133
8.2.5. Fans......Page 134
8.3. Community Structure......Page 135
9. Spatial Aspects......Page 137
10. Conclusion and Perspectives......Page 140
References......Page 141
1. Introduction......Page 148
2. Methodologies to Obtain Protein Interaction Data......Page 149
2.1. Experimental Technologies to Identify Protein-Protein Interactions......Page 150
2.2. Computational Approaches to Predict Protein-Protein Interactions......Page 153
3.1. Visualization of Protein Interaction Networks......Page 156
3.2. Topological Properties of Protein Interaction Networks......Page 159
3.3. Integrating Protein Interaction Networks with Complementary Data......Page 162
3.4. Network Alignment and Comparison......Page 163
4. Robustness of Protein Interaction Networks......Page 167
5. Evolution of Protein Interaction Networks......Page 168
6. Perspectives......Page 170
References......Page 171
1. Introduction......Page 178
2. Interacting Partners......Page 179
3.1. Mathematical Representation......Page 184
3.2. Defining the Biological System......Page 185
4.1. Sources of Data......Page 190
4.2.2. Flux Balance Analyses......Page 192
4.3. Are Paths in a Graph Metabolic Pathways?......Page 194
5.1. Introduction......Page 196
5.2. Small World Characteristics......Page 197
5.3. Short Path Lengths......Page 198
5.4. Power Law Connectivity......Page 199
5.5. Modular Structure......Page 200
5.6. Evolution of the Structure......Page 202
5.7. Robustness and Damage......Page 203
6. Dynamics......Page 205
6.1. Distribution of Fluxes......Page 206
7. Conclusion and Perspective......Page 207
References......Page 208
1. Introduction......Page 214
2.1. Types of Component Networks......Page 216
2.2. Types of Composite Networks......Page 218
3.1. Obtaining the Component Networks......Page 220
3.2. Reconstructing the Composite Network......Page 222
4. Computational Modeling......Page 223
4.1. Topological Properties of Composite Networks can be Assessed Using Graph-based Models......Page 224
4.2. Structural Properties of Steady-state Dynamics can be Assessed Using Stoichiometric Models of Metabolism......Page 225
5. Logical Functions as a Unifying Framework for Steady State Dynamics of Regulation and Metabolism......Page 228
6. Topology of Composite Networks......Page 229
6.1. Correlation Between Topological Properties in Pairs of Coupled Networks......Page 230
6.1.1. Metabolism and Coexpression......Page 233
6.1.2. Protein-Protein Interactions and Coexpression......Page 234
6.1.3. Genetic Interactions and Physical Interactions......Page 235
6.2. Topological Features of the Composite Network......Page 237
6.2.1. Layered Structure of the Protein Interaction Network......Page 239
6.2.2. Modules Defined over Composite Networks......Page 240
7. Dynamics......Page 248
7.1. Interplay Between the Dynamics of Component Networks......Page 249
7.1.1. Correlation Between Steady State Fluxes and mRNA Expression Levels Assessed Using Stoichiometric Models of Metabolism......Page 250
7.1.2. Synchronization Between Complex Formation and Cell-Cycle Regulation......Page 251
7.2. Investigating the Dynamics of Heterogeneous Networks......Page 253
8. Interaction Prediction and Network Refinement......Page 254
8.1. Filling Gaps in Metabolic Networks......Page 255
8.2. Predicting Genetic Interactions......Page 257
8.3. Predicting Protein-Protein Interactions......Page 259
8.4. Refining the Structure and Logic of Transcriptional Regulation Network......Page 262
9. Conclusions and Perspectives......Page 264
Acknowledgements......Page 265
References......Page 266
1. Introduction......Page 272
2. Definitions......Page 275
3.1. Transcription Factors: DNA-Binding Domains......Page 276
3.2. Transcription Factors: Dimerisation Domains......Page 278
3.3. Transcription Factors: Effector Domains......Page 279
3.5. Evolution of Promoters......Page 280
3.6. Co-evolution and Decoupled Evolution......Page 283
4. Motifs to Modules to Networks......Page 284
5. Models of Network Evolution and Simulation Studies......Page 288
5.1. Simulation Principles......Page 289
5.2. Duplication–Mutation Models......Page 290
5.3. Mutation-Only Models......Page 293
5.4. Using Subgraph Count......Page 294
5.5. Models Motivated by Pattern Formation......Page 296
6. Conclusions......Page 297
References......Page 299
1. Introduction......Page 306
1.1. Interacting Partners......Page 309
2.1. Synaptic Transmission......Page 315
2.2. Synaptic Dynamics and Plasticity......Page 317
2.3. Interdependency Between Intrinsic Excitability and Extrinsic Synaptic Factors......Page 320
3.1. Modelling Cells and Synaptic Interactions......Page 323
3.2. Modelling Networks......Page 324
4.1. Diversity of Structural Network Topology......Page 326
4.2. Complexity of Structural Network Topology......Page 330
4.3. Structural Network Topology and the 'Small-World' Analogy......Page 331
4.4. Functional Network Topology and the 'Scale-Free' Analogy......Page 334
5.1. A Possible Role of 'Noise' in the Functional Dynamics of Cortical Networks......Page 337
5.2. Self-Organisation and Adaptive Properties in Network Dynamics......Page 340
6. Conclusion and Perspectives: Complexity as a Computational Principle?......Page 342
References......Page 344
1. Introduction......Page 356
2. Outline of the Biology of the Immune Response......Page 359
3. The Components of the Immune System......Page 362
4.1. Gene Networks......Page 365
4.3. Intercellular Signalling Networks......Page 366
4.4. Networks of Microenvironments......Page 368
5. Integration Between Different Levels......Page 371
6. Modelling Immune Networks......Page 372
References......Page 376
1. Introduction......Page 380
2. The Pioneers......Page 384
3. Energy Based Approaches......Page 390
4. Complexity and Stability......Page 395
5. The Topological Structure of Food Webs......Page 404
6. Indirect Effects......Page 415
7. Networking with Non Trophic Interactions......Page 418
8. Future Avenues of Research......Page 420
Acknowledgements......Page 421
References......Page 422
1. Introduction......Page 438
2. Models......Page 440
2.1. Structural Models and Food-Web Topology......Page 441
2.2. Bioenergetic Model of Nonlinear Food-Web Dynamics......Page 442
3. Topology and Dynamics......Page 446
3.1. Effects of Structure on Dynamics......Page 453
3.2. Effects of Dynamics on Structure......Page 454
3.3. Omnivory......Page 457
4. Conclusion......Page 458
References......Page 459
1. Introduction......Page 464
2. Inference Robustness Assessment......Page 467
2.1. Transitions within Mass Action Models from Continuous Population Deterministic to Discrete Individual Stochastic Models......Page 469
2.2. Stochastic Compartmental Mass Action — Network Transitions......Page 470
2.3. Dynamic Network Models......Page 475
2.4. ODE Network Models of Correlation in Infection Status......Page 476
2.5. Mathematical Analysis of Network Models with other Structures......Page 478
2.6. Transiting from Undirected to Directed Graphs......Page 480
2.7. Models Involving Contact Processes That Generate Networks......Page 482
2.8. Statistical Analysis of Network Structure......Page 485
2.9. Designing Network Models with Robustness Assessment in Mind......Page 487
3. Nucleotide Sequence Traces through Contact Networks......Page 488
4. Risk Factors for Transmissibility......Page 491
5.1. Infection Processes and Infectious Agent Characteristics......Page 494
5.2. Modes of Transmission......Page 497
5.3. Interacting Partners in Infection Transmission Network Models......Page 500
5.4.1. Micro-network Interview Data......Page 501
5.4.3. Environmental Contamination Data......Page 505
5.5. Computational Modeling......Page 506
5.6. Macro Topology of the Network......Page 508
5.8. Spatial Aspects......Page 510
6. Conclusion and Perspective......Page 511
References......Page 513
Index......Page 522
