Handbook on Biological Networks (World Scientific Lecture Notes in Complex Systems)

Contents......Page 8
Preface......Page 6
1. Introduction......Page 12
Networks at the Cellular Level......Page 18
2.1. Introduction......Page 20
2.2. Regulatory Networks......Page 21
2.2.2. Computational approach......Page 22
2.3.1. Experimental approach......Page 24
2.3.2. Computational approach......Page 27
2.4. Protein-Protein Interaction Networks......Page 28
2.4.2. Computational approach......Page 29
2.5. Evolutionary Networks......Page 30
2.5.1. Computational approach......Page 31
2.6. Statistical Properties of Biological Networks......Page 32
2.7. Functional Networks Motifs......Page 36
2.8. Conclusion......Page 37
References......Page 38
3.1. Introduction......Page 44
3.2. Coupled Repressilators......Page 45
3.2.1.1. Model......Page 46
3.2.1.2. Transition to synchronization......Page 48
3.2.2. Phase-repulsive coupling......Page 49
3.2.2.1. Bifurcation analysis for two coupled repressilators......Page 50
3.2.2.2. Comparison between bifurcation analysis and direct calculations......Page 53
3.2.2.4. Large system sizes......Page 55
3.3.1. Dynamical regimes of coupled relaxators......Page 57
3.3.2. Bifurcation analysis......Page 60
3.3.3. Response to external noise: quantized cycling time......Page 62
3.4. Conclusions and Discussion......Page 63
References......Page 66
4.1. Introduction......Page 70
4.2. Boolean Network Concepts and History......Page 72
4.3. Extensions of the Classical Boolean Framework......Page 75
4.4. Boolean Inference Methods and Examples in Biology......Page 77
4.6. Dynamic Boolean Models: Examples in Plant Biology, Developmental Biology and Immunology......Page 79
4.7. Conclusions......Page 85
References......Page 87
5.1. Introduction......Page 90
5.2. Noisy Dynamics of Boolean Networks......Page 92
5.4. Model Signalling Networks......Page 94
5.4.1. Topology......Page 95
5.4.2. Boolean rules......Page 96
5.4.3. Noisy dynamics......Page 97
5.4.4. Complex dynamics from simple models......Page 99
5.4.5. Robustness......Page 100
5.5. Dynamics in Real Genetic Regulatory Networks......Page 103
5.5.1. A GRN underlying cell-fate determination during early stages of ower development......Page 106
5.5.2. Noisy dynamics......Page 107
References......Page 110
6.1. Introduction......Page 114
6.2. The Connectivity Graph of the Energy Landscape......Page 116
6.2.1. Renormalization of the graph......Page 118
6.2.2. Two-dimensional models......Page 120
6.2.2.1. Topological properties of the renormalized connectivity graph......Page 121
6.2.3. Three-dimensional models......Page 128
6.3. Geometry of Energy Landscapes......Page 129
6.3.1. Curvature of the energy landscape of simple model proteins......Page 131
References......Page 137
7.1. Introduction......Page 140
7.2.1. Statistical mechanical foundations......Page 143
7.2.2. Anisotropic network models......Page 146
7.2.3. Gaussian network model......Page 147
7.2.4. Rigid block models......Page 149
7.2.5. Treatment of perturbations......Page 151
7.2.6. Langevin dynamics......Page 153
7.3.1. Membrane proteins......Page 154
7.3.2. Viruses......Page 162
7.4. Conclusion......Page 165
References......Page 166
8. Metabolic Networks Maria Concetta Palumbo, Lorenzo Farina, Alfredo Colosimo, and Alessandro Giuliani......Page 170
8.1. Introduction......Page 171
8.2.1. Network topology and effect of mutations......Page 177
8.2.2. Evolution of network topologies......Page 184
8.3. Network Dynamics......Page 192
8.3.1. Representing dynamical schemes......Page 193
8.3.2. Elementary flux modes......Page 194
8.3.3. Flux balance analysis......Page 196
8.3.4. MOMA, PhPP and ROOM analyses......Page 197
8.3.5. Metabolic control analysis......Page 198
8.4. Conclusion......Page 199
References......Page 200
Brain Networks......Page 208
9.1. Introduction......Page 210
9.2. Structural Connectivity of the Human Cerebral Cortex......Page 213
9.3. Dynamic Brain Networks......Page 217
9.4. Network Topology and Network Disease......Page 223
9.5. Conclusion......Page 224
References......Page 225
10. Brain Network Analysis from High-Resolution EEG Signals Fabrizio De Vico Fallani and Fabio Babiloni......Page 228
10.1. Cortical Activity Estimation......Page 229
10.1.2. Estimation of cortical source current density......Page 230
10.2. Functional Connectivity Estimation......Page 231
10.2.1. MultiVariate AutoRegressive models......Page 232
10.2.1.2. Partial directed coherence......Page 233
10.3.1. Network density......Page 235
10.3.3. Strength distributions......Page 236
10.3.5. Motifs......Page 237
10.3.6. Network structure......Page 238
10.4.1. Cortical network structure in tetraplegics......Page 239
10.4.2. Time-varying cortical network during foot movement......Page 243
References......Page 250
11.2. The Dataset......Page 254
11.3. Optimization Model Problem......Page 256
11.4. Results and Discussion......Page 258
11.4.1. Experiment 1......Page 259
11.4.2. Experiment 2......Page 260
11.4.4. Experiment 4......Page 261
11.4.7. Experiment 7......Page 262
11.4.8. Summary......Page 265
References......Page 267
12.1. Cultured Neuronal Networks......Page 268
12.2. Recording the Network Activity......Page 269
12.3. Network Engineering......Page 270
12.4. The Formation of Synchronized Bursting Events......Page 272
12.5. The Characterization of the SBEs......Page 274
12.7. Function—Form Relations in Cultured Networks......Page 276
12.8. Analyzing the SBEs Motifs......Page 279
12.9 Network Repertoire......Page 280
12.10. Network under Hypothermia......Page 281
12.11. Summary......Page 284
Acknowledgments......Page 286
References......Page 287
13.1. Precise Timing in the Brain?......Page 290
13.2. Feed-Forward Mechanisms: Synfire Chains......Page 292
13.3.1. An analytically accessible class of models......Page 294
13.3.2. Related models......Page 297
13.4.1. Quiescence and synchrony......Page 298
13.4.2. Away from synchrony: first hints towards spike patterns in recurrent networks......Page 300
13.4.3. Asynchrony: Irregular, chaotic, and balanced activity......Page 301
13.5. Precise Timing in Recurrent Networks......Page 302
13.5.2. Realizing spike patterns in complex networks — An inverse problem......Page 303
13.6. Conclusions and Open Questions......Page 305
References......Page 307
Networks at the Individual and Population Levels......Page 316
14.1. Introduction......Page 318
14.2.1. The structure of food webs......Page 320
14.2.2. The scale of food-web stability......Page 322
14.2.3. Whole food-web dynamics......Page 323
14.3. Mutualistic Networks......Page 324
14.3.1. The structure of mutualistic networks......Page 325
14.3.2. Assembly of mutualistic networks......Page 326
14.3.3. Models of mutualistic-network disassembly......Page 328
14.4.1. The structure of spatial networks......Page 330
14.4.2. Unraveling the dynamics of spatial networks......Page 333
14.5. Concluding Remarks......Page 334
References......Page 335
15.1. Introduction......Page 340
15.2.1. Modeling living individuals......Page 341
15.2.2. The sequence space......Page 345
15.2.3. Mutations......Page 347
15.2.4. The phenotype......Page 349
15.2.5. Fitness and the mutation-selection equation......Page 350
15.3. Evolution on a Fitness Landscape......Page 352
15.3.1. Evolution and optimization, replicator equation......Page 354
15.3.2. Evolution and statistical mechanics......Page 355
15.3.3. Quasispecies, error threshold and Muller's ratchet......Page 357
15.3.4. Evolution in a phenotypic space......Page 360
15.3.4.1. Evolution near a smooth and sharp maximum......Page 361
15.3.5. Coexistence on a static tness landscape......Page 364
15.3.7. A simple example of tness landscape......Page 365
15.3.9. Speciation in a at landscape......Page 367
15.4. Ageing......Page 368
15.5.1. Speciation in the phenotypic space......Page 369
15.5.2. Speciation and mutational meltdown in the hyper-cubic genotypic space......Page 373
15.5.3. Sex......Page 375
15.5.4. Game theory......Page 376
15.5.6. Self-organization of ecosystems......Page 378
References......Page 380
16. Evolution of Cooperation in Adaptive Social Networks Sven Van Segbroeck, Francisco C. Santos, Arne Traulsen, Tom Lenaerts, and Jorge M. Pacheco......Page 384
16.1. Introduction......Page 385
16.2.1. Linking dynamics......Page 386
16.2.2. Strategy dynamics......Page 387
16.2.3.1. Fast strategy dynamics......Page 389
16.2.3.2. Fast linking dynamics......Page 390
16.2.4. Comparable timescales......Page 391
16.3.1. Specification of the linking dynamics......Page 392
16.3.2. Numerical results......Page 394
16.4.1. Specification of the linking dynamics......Page 396
16.4.2. Numerical results......Page 398
16.5. Discussion......Page 400
References......Page 401
17.1. Collective Behavior and Motion Coordination in Animal Groups......Page 404
17.2. Mathematical Models for Collective Motion......Page 406
17.3. Applying Complex Network Theory to Coordination Models......Page 411
17.3.1. Effect of long-range connections in the Vicsek's model......Page 412
17.3.2. Effect of long-range connections in the Couzin's model......Page 416
17.3.3. Proximity graphs and topological interactions......Page 419
17.4. From Biological Networks to Engineering Problems......Page 421
References......Page 423
18.1. Introduction......Page 428
18.2. Epidemics on Networks......Page 430
18.3. An Adaptive SIS Model......Page 432
18.4.1. Basic moment-expansion of the model......Page 434
18.4.2. Dynamics of the adaptive SIS model......Page 437
18.4.3. Accuracy and extensions of the moment closure approximation......Page 439
18.5. Interpretation and Extensions of the Model......Page 440
18.6. Summary and Conclusions......Page 444
References......Page 445
Index......Page 448