Handbook of Statistical Systems Biology

HANDBOOK OF STATISTICAL SYSTEMS BIOLOGY......Page 3
Contents......Page 7
Preface......Page 19
Contributors......Page 21
Part A: METHODOLOGICAL CHAPTERS......Page 25
1.1 Introduction......Page 27
1.2 Cell signaling systems......Page 28
1.3 The challenge of many moving parts......Page 29
1.4 The challenge of parts with parts......Page 31
1.5 Closing remarks......Page 33
References......Page 34
2.1 Introduction......Page 39
2.2.1 Models for dependent data......Page 40
2.2.2 Multiple testing......Page 43
2.3.1 Building a classifier......Page 46
2.3.2 Aggregation......Page 49
2.3.3 Regularization......Page 51
2.3.4 Performance assessment......Page 53
2.4.1 Geometric methods......Page 55
2.4.2 (Discrete) latent variable models......Page 56
2.4.3 Inference......Page 57
References......Page 60
3.2.1 Bases......Page 63
3.2.2 Bayesian analysis in action......Page 65
3.2.3 Prior distributions......Page 66
3.2.4 Confidence intervals......Page 70
3.3.2 The Bayes factor......Page 72
3.3.3 Point null hypotheses......Page 73
3.3.4 The ban on improper priors......Page 74
3.3.5 The case of nuisance parameters......Page 76
3.3.6 Bayesian multiple testing......Page 78
3.4.1 Prediction......Page 79
3.4.3 Model choice......Page 80
3.5.1 Computational challenges......Page 81
3.5.2 Monte Carlo methods......Page 83
3.5.3 MCMC methods......Page 85
3.5.4 Approximate Bayesian computation techniques......Page 87
References......Page 88
4.1 Storing knowledge: Experimental data, knowledge databases, ontologies and annotation......Page 90
4.1.1 Data repositories......Page 91
4.1.2 Knowledge Databases......Page 92
4.1.3 Ontologies......Page 94
4.1.4 Annotation......Page 95
4.2.1 Integration of experimental data......Page 98
4.3 Concluding remarks......Page 101
References......Page 102
5.1 Dynamical models for network inference......Page 107
5.1.1 Linear models......Page 108
5.1.2 Nonlinear models......Page 109
5.2.1 Least squares......Page 113
5.2.2 Methods based on least squares......Page 114
5.2.3 Dealing with noise: CTLS......Page 115
5.2.4 Convex optimization methods......Page 121
5.2.5 Sparsity pattern of the discrete-time model......Page 124
5.2.6 Application examples......Page 125
5.3 Reconstruction methods based on nonlinear models......Page 128
5.3.1 Approaches based on polynomial and rational models......Page 129
5.3.2 Approaches based on S-systems......Page 131
5.3.3 A case-study......Page 133
References......Page 135
6.1 Introduction......Page 138
6.2 Overview of chapter......Page 139
6.3 Computational algebra......Page 140
6.4.1 Definitions......Page 142
6.4.2 Further examples......Page 143
6.5 Parameter inference......Page 146
6.6 Model invariants......Page 148
6.7 Log-linear models......Page 150
6.8 Reverse engineering of networks......Page 153
References......Page 154
Part B: TECHNOLOGY-BASED CHAPTERS......Page 157
7.1 Biological background......Page 159
7.2.1 Microarray technology......Page 160
7.2.3 High throughput sequencing (HTS)......Page 161
7.2.4 mRNA expression estimates from HTS......Page 163
7.3.1 Common approaches for significance testing......Page 164
7.3.2 Moderated statistics......Page 165
7.3.3 Statistics for HTS......Page 166
7.3.4 Multiple testing corrections......Page 167
7.3.5 Filtering genes......Page 169
7.4.1 Gene-set analysis......Page 171
7.4.2 Dimensionality reduction......Page 173
7.4.3 Clustering......Page 174
7.5.1 Variable selection......Page 177
7.5.2 Estimating the performance of a model......Page 180
References......Page 181
8.1 Introduction......Page 187
8.2.1 Analytical technologies......Page 188
8.2.2 Preprocessing......Page 190
8.3.1 Unsupervised methods......Page 193
8.3.2 Supervised methods......Page 195
8.3.3 Metabolome-wide association studies......Page 196
8.3.4 Metabolic correlation networks......Page 197
8.3.5 Simulation of metabolic profile data......Page 200
References......Page 202
9.1.1 Intracellular signal transduction......Page 205
9.2 Measurement techniques......Page 206
9.2.2 Immunocytochemistry......Page 207
9.2.3 Flow cytometry......Page 208
9.2.4 Fluorescent microscope......Page 209
9.2.5 Live cell imaging......Page 211
9.2.6 Fluorescent probes for live cell imaging......Page 212
9.2.7 Image cytometry......Page 214
9.2.8 Image processing......Page 215
9.3.1 Time series (mean, variation, correlation, localization......Page 218
9.4 Summary......Page 221
References......Page 223
10.1 Introduction......Page 224
10.2.1 Protein structure and function......Page 225
10.2.3 Experimental techniques for interaction detection......Page 226
10.2.4 Computationally predicted data-sets......Page 227
10.2.6 Error in PPI data......Page 228
10.3.1 Graphs......Page 229
10.3.2 Network summary statistics......Page 230
10.3.4 Models of random networks......Page 231
10.3.6 Approximate Bayesian Computation......Page 233
10.3.7 Threshold behaviour in graphs......Page 234
10.4.1 Network comparison based on subgraph counts......Page 235
10.4.2 Network alignment......Page 237
10.4.3 Using functional annotation for network alignment......Page 239
10.5.1 How evolutionary models affect network alignment......Page 241
10.6 Community detection in PPI networks......Page 242
10.6.1 Community detection methods......Page 243
10.6.2 Evaluation of results......Page 244
10.7 Predicting function using PPI networks......Page 245
10.8 Predicting interactions using PPI networks......Page 247
10.8.2 Using triangles for predicting interactions......Page 248
10.9.1 Dynamics......Page 250
10.9.3 Limitations of models, prediction and alignment methods......Page 251
References......Page 252
Part C: NETWORKS AND GRAPHICAL MODELS......Page 259
11.1 Graphical structures and random variables......Page 261
11.2 Learning graphical models......Page 265
11.2.1 Structure learning......Page 266
11.3 Inference on graphical models......Page 270
11.4.1 Correlation networks......Page 271
11.4.2 Covariance selection networks......Page 272
11.4.5 Other graphical models......Page 274
References......Page 275
12.1.1 Regulatory networks in biology......Page 279
12.2.1 Genetic network modelling with DBNs......Page 280
12.2.2 DBN for linear interactions and inference procedures......Page 283
12.3 Go forward: how to recover the structure changes with time......Page 285
12.3.1 ARTIVA network model......Page 286
12.3.2 ARTIVA inference procedure and performance evaluation......Page 287
12.4 Discussion and Conclusion......Page 291
References......Page 292
13.1.1 Basic concepts......Page 294
13.1.2 Dynamic Bayesian networks......Page 296
13.2 Inclusion of biological prior knowledge......Page 297
13.2.1 The ‘energy’ of a network......Page 298
13.2.2 Prior distribution over network structures......Page 299
13.2.3 MCMC sampling scheme......Page 300
13.2.5 Empirical evaluation on the Raf signalling pathway......Page 301
13.3.1 Motivation: Inferring spurious feedback loops with DBNs......Page 305
13.3.2 A nonlinear/nonhomogeneous DBN......Page 306
13.3.4 Simulation results......Page 308
13.3.5 Results on Arabidopsis gene expression time series......Page 309
13.4 Discussion......Page 311
References......Page 312
14.1 Background and motivation......Page 314
14.2 What do we want from a PPI network?......Page 317
14.3.1 Lock and key......Page 318
14.3.2 Geometric networks......Page 321
14.4 Range-dependent graphs......Page 325
14.5 Summary......Page 329
References......Page 330
15.1 Introduction......Page 333
15.2.1 Local network features and their statistics......Page 334
15.2.2 Examples......Page 335
15.3.1 Network families, hypothesis testing and null models......Page 336
15.3.2 Tailored random graph ensembles......Page 337
15.4.1 Network complexity......Page 339
15.4.2 Information-theoretic dissimilarity......Page 340
15.5 Applications to PPINs......Page 341
15.5.2 Mapping PPIN data biases......Page 344
15.6.1 Generating random graphs via Markov chains......Page 347
15.6.2 Degree-constrained graph dynamics based on edge swaps......Page 348
15.7 Discussion......Page 349
References......Page 351
Part D: DYNAMICAL SYSTEMS......Page 355
16.1 Introduction......Page 357
16.3 The natural measure......Page 358
16.4 The Kolmogorov–Sinai entropy......Page 359
16.5 Symbolic dynamics......Page 360
References......Page 362
17.1 Introduction......Page 363
17.2 Basic solution types......Page 367
17.3 Qualitative behaviour......Page 370
17.4 Stability and bifurcations......Page 371
17.5 Ergodicity......Page 377
17.6 Timescales......Page 378
17.7 Time series analysis......Page 380
References......Page 381
18.2.1 Low copy number......Page 383
18.3.2 Markov jump process......Page 384
18.3.3 Diffusion approximation......Page 388
18.3.5 Modelling extrinsic noise......Page 389
18.4.1 Likelihood-based inference......Page 390
18.4.2 Partial observation and data augmentation......Page 391
18.4.3 Data augmentation MCMC approaches......Page 392
18.4.4 Likelihood-free approaches......Page 393
18.4.5 Approximate Bayesian computation......Page 394
18.4.8 Stochastic model emulation......Page 395
18.5 Conclusions......Page 396
References......Page 397
19.1 Introduction......Page 400
19.1.1 A simple systems biology model......Page 401
19.2 Generalized linear model......Page 403
19.2.1 Fitting basis function models......Page 404
19.2.2 An infinite basis......Page 407
19.2.3 Gaussian processes......Page 409
19.3 Model based target ranking......Page 411
19.4 Multiple tanscription factors......Page 415
19.5 Conclusion......Page 417
References......Page 418
20 Model Identification by Utilizing Likelihood-Based Methods......Page 419
20.1 ODE models for reaction networks......Page 420
20.1.1 Rate equations......Page 421
20.2 Parameter estimation......Page 422
20.2.1 Sensitivity equations......Page 423
20.2.2 Testing hypothesis......Page 424
20.2.3 Confidence intervals......Page 425
20.3.1 Structural nonidentifiability......Page 427
20.3.2 Practical nonidentifiability......Page 428
20.4 The profile likelihood approach......Page 429
20.4.1 Experimental design......Page 430
20.4.3 Observability and confidence intervals of trajectories......Page 431
20.4.4 Application......Page 432
20.5 Summary......Page 437
Acknowledgements......Page 438
References......Page 439
Part E: APPLICATION AREAS......Page 441
21.1 Introduction......Page 443
21.2 Overview of inference techniques......Page 444
21.3 Parameter inference and model selection for dynamical systems......Page 446
21.3.1 Model selection......Page 448
21.4 Approximate Bayesian computation......Page 449
21.5 Application: Akt signalling pathway......Page 450
21.5.1 Exploring different distance functions......Page 452
21.5.4 Sensitivity analysis by principal component analysis (PCA)......Page 454
21.6 Conclusion......Page 459
References......Page 460
22 Modelling Transcription Factor Activity......Page 464
22.1 Integrating an ODE with a differential operator......Page 465
22.2.1 Taking into account the nature of the biological system being modelled......Page 467
22.2.2 Bounds choice for polynomial interpolation......Page 469
22.3 Applications......Page 471
22.4 Estimating intermediate points......Page 473
References......Page 474
23.1 Introduction......Page 475
23.3 Metabolic models......Page 477
23.4 Protein–protein interactions......Page 479
23.5 Response to environment......Page 481
23.6 Immune system interactions......Page 482
23.7 Manipulation of other host systems......Page 483
23.8 Evolution of the host–pathogen system......Page 484
23.10 Concluding remarks......Page 486
References......Page 487
24.1 Introduction......Page 491
24.2 The challenge of metabolite identification......Page 492
24.3 Bayesian analysis of metabolite mass spectra......Page 493
24.4 Incorporating additional information......Page 495
24.5 Probabilistic peak detection......Page 496
24.6 Statistical inference......Page 497
24.7 Software development for metabolomics......Page 498
References......Page 499
25.2 Current approaches in microRNA Systems Biology......Page 501
25.3 Experimental findings and data that guide the developments of computational tools......Page 502
25.4 Approaches to microRNA target predictions......Page 503
25.5.1 Identifying microRNA activity from mRNA expression......Page 506
25.5.2 Modeling combinatorial microRNA regulation from joint microRNA and mRNA expression data......Page 508
25.6 Network approach for studying microRNA-mediated regulation......Page 509
25.7 Kinetic modeling of microRNA regulation......Page 510
25.7.1 A basic model of microRNA-mediated regulation......Page 511
25.7.2 Estimating fold-changes of mRNA and proteins in microRNA transfection experiments......Page 512
25.7.5 Reconstructing microRNA kinetics......Page 513
25.8 Discussion......Page 514
References......Page 515
Index......Page 519