Principles of Computational Cell Biology: From Protein Complexes to Cellular Networks

Cover......Page 1
Title Page......Page 5
Copyright......Page 6
Contents......Page 7
Preface of the First Edition......Page 17
Preface of the Second Edition......Page 19
1.1 Some Basics About Networks......Page 21
1.1.2 Small‐World Phenomenon......Page 22
1.1.3 Scale‐Free Networks......Page 23
1.2 Biological Background......Page 24
1.2.2 Cellular Components......Page 25
1.2.3 Spatial Organization of Eukaryotic Cells into Compartments......Page 27
1.3.1 Biochemical Pathways......Page 28
1.3.3 Signal Transduction......Page 31
1.4.1 Ontologies......Page 32
1.4.4 Reactome......Page 33
1.4.6 DAVID......Page 34
1.4.8 Systems Biology Markup Language......Page 35
1.7 Problems......Page 37
Bibliography......Page 38
Chapter 2 Structures of Protein Complexes and Subcellular Structures......Page 41
2.1 Examples of Protein Complexes......Page 42
2.1.1 Principles of Protein–Protein Interactions......Page 44
2.1.2 Categories of Protein Complexes......Page 47
2.2.1 Complexome of Saccharomyces cerevisiae......Page 48
2.2.2 Bacterial Protein Complexomes......Page 50
2.3 Experimental Determination of Three‐Dimensional Structures of Protein Complexes......Page 51
2.3.1 X‐ray Crystallography......Page 52
2.3.4 Cryo‐EM......Page 54
2.3.6 Fluorescence Resonance Energy Transfer......Page 55
2.3.7 Mass Spectroscopy......Page 56
2.4.1 Correlation‐Based Density Fitting......Page 58
2.5.1 Fourier Series......Page 60
2.5.4 Convolution Theorem......Page 61
2.5.5 Fast Fourier Transformation......Page 62
2.6 Advanced Density Fitting......Page 64
2.6.1 Laplacian Filter......Page 65
2.7 FFT Protein–Protein Docking......Page 66
2.8 Protein–Protein Docking Using Geometric Hashing......Page 68
2.9.1 CombDock......Page 69
2.9.3 3D‐MOSAIC......Page 72
2.10 Electron Tomography......Page 73
2.10.2 Protein Complexes in Mycoplasma pneumoniae......Page 75
2.11 Summary......Page 76
2.12.1 Mapping of Crystal Structures into EM Maps......Page 77
Bibliography......Page 80
3.1 Modeling by Homology......Page 83
3.2.1 Size and Shape......Page 86
3.2.2 Composition of Binding Interfaces......Page 88
3.2.3 Hot Spots......Page 89
3.2.4 Physicochemical Properties of Protein Interfaces......Page 91
3.2.5 Predicting Binding Affinities of Protein–Protein Complexes......Page 92
3.2.6 Forces Important for Biomolecular Association......Page 93
3.3.1 Pairing Propensities......Page 95
3.3.2 Statistical Potentials for Amino Acid Pairs......Page 98
3.3.3 Conservation at Protein Interfaces......Page 99
3.3.4 Correlated Mutations at Protein Interfaces......Page 103
Bibliography......Page 106
4.1 Primer on Mathematical Graphs......Page 109
4.2 A Few Words About Algorithms and Computer Programs......Page 110
4.2.1 Implementation of Algorithms......Page 111
4.2.2 Classes of Algorithms......Page 112
4.3 Data Structures for Graphs......Page 113
4.4 Dijkstra's Algorithm......Page 115
4.4.1 Description of the Algorithm......Page 116
4.4.2 Pseudocode......Page 120
4.5 Minimum Spanning Tree......Page 121
4.6 Graph Drawing......Page 122
4.7 Summary......Page 124
4.8 Problems......Page 125
4.8.1 Force Directed Layout of Graphs......Page 127
Bibliography......Page 130
5.1 Experimental High‐Throughput Methods for Detecting Protein–Protein Interactions......Page 131
5.1.2 Two‐Dimensional Gel Electrophoresis......Page 132
5.1.3 Affinity Chromatography......Page 133
5.1.4 Yeast Two‐hybrid Screening......Page 134
5.1.5 Synthetic Lethality......Page 135
5.1.8 Overlap of Interactions......Page 136
5.1.9 Criteria to Judge the Reliability of Interaction Data......Page 138
5.2 Bioinformatic Prediction of Protein–Protein Interactions......Page 140
5.2.2 Phylogenetic Profiling/Coevolutionary Profiling......Page 141
5.2.2.1 Coevolution......Page 142
5.3 Bayesian Networks for Judging the Accuracy of Interactions......Page 144
5.3.2 Bayesian Network......Page 145
5.3.3 Application of Bayesian Networks to Protein–Protein Interaction Data......Page 146
5.3.3.2 Prior and Posterior Odds......Page 147
5.3.3.3 A Worked Example: Parameters of the Naïve Bayesian Network for Essentiality......Page 148
5.3.3.4 Fully Connected Experimental Network......Page 149
5.4.2 Protein Interaction Network of Escherichia coli......Page 151
5.5 Protein Domain Networks......Page 152
5.6 Summary......Page 155
5.7.1 Bayesian Analysis of (Fake) Protein Complexes......Page 156
Bibliography......Page 158
6.1.1 Degree Distribution......Page 161
6.1.2 Clustering Coefficient......Page 163
6.2 Finding Cliques......Page 165
6.3 Random Graphs......Page 166
6.4 Scale‐Free Graphs......Page 167
6.5 Detecting Communities in Networks......Page 169
6.5.1 Divisive Algorithms for Mapping onto Tree......Page 173
6.6 Modular Decomposition......Page 175
6.6.1 Modular Decomposition of Graphs......Page 177
6.7.1 MCODE......Page 181
6.7.2 ClusterONE......Page 182
6.7.3 DACO......Page 183
6.7.4 Analysis of Target Gene Coexpression......Page 184
6.8 Network Growth Mechanisms......Page 185
6.10 Problems......Page 189
Bibliography......Page 198
7.1 Transcription Factors......Page 201
7.3.1 Electrophoretic Mobility Shift Assay......Page 203
7.3.2 DNAse Footprinting......Page 204
7.3.3 Protein‐Binding Microarrays......Page 205
7.4 Position‐Specific Scoring Matrices......Page 207
7.5 Binding Free Energy Models......Page 209
7.6 Cis‐Regulatory Motifs......Page 211
7.7 Relating Gene Expression to Binding of Transcription Factors......Page 212
7.9 Problems......Page 214
Bibliography......Page 215
8.1 Regulation of Gene Transcription at Promoters......Page 217
8.2 Experimental Analysis of Gene Expression......Page 218
8.2.2 Microarray Analysis......Page 219
8.3 Statistics Primer......Page 221
8.3.3 Fisher's Exact Test......Page 223
8.3.4 Mann–Whitney–Wilcoxon Rank Sum Tests......Page 225
8.3.6 Hypergeometric Test......Page 226
8.4.1 Removal of Outlier Genes......Page 227
8.4.3 Log Transformation......Page 228
8.5 Differential Expression Analysis......Page 229
8.5.2 SAM Analysis of Microarray Data......Page 230
8.5.3 Differential Expression Analysis of RNA‐seq Data......Page 232
8.5.3.2 DESeq......Page 233
8.6 Gene Ontology......Page 234
8.6.1 Functional Enrichment......Page 236
8.8 Translation of Proteins......Page 237
8.8.1 Transcription and Translation Dynamics......Page 238
8.9 Summary......Page 239
8.10 Problems......Page 240
Bibliography......Page 244
Chapter 9 Gene Regulatory Networks......Page 247
9.1.1 Gene Regulatory Network of E. coli......Page 248
9.2 Graph Theoretical Models......Page 251
9.2.1 Coexpression Networks......Page 252
9.2.2 Bayesian Networks......Page 253
9.3.1 Boolean Networks......Page 254
9.3.2 Reverse Engineering Boolean Networks......Page 255
9.3.3 Differential Equations Models......Page 256
9.4 DREAM: Dialogue on Reverse Engineering Assessment and Methods......Page 258
9.4.1 Input Function......Page 259
9.4.2 YAYG Approach in DREAM3 Contest......Page 260
9.5 Regulatory Motifs......Page 264
9.5.2 SIM......Page 265
9.5.3 Densely Overlapping Region (DOR)......Page 266
9.6.1 Key‐pathway Miner Algorithm......Page 267
9.6.2 Identifying Sets of Dominating Nodes......Page 268
9.6.4 Minimum Connected Dominating Set......Page 269
9.7 Summary......Page 270
9.8 Problems......Page 271
Bibliography......Page 274
10.1 Introduction to RNAs......Page 277
10.2 Elements of RNA Interference: siRNAs and miRNAs......Page 279
10.3 miRNA Targets......Page 281
10.5 Role of TFs and miRNAs in Gene‐Regulatory Networks......Page 284
10.6 Constructing TF/miRNA Coregulatory Networks......Page 286
10.6.1 TFmiR Web Service......Page 287
10.6.1.1 Construction of Candidate TF–miRNA–Gene FFLs......Page 288
10.6.1.2 Case Study......Page 289
Bibliography......Page 290
11.1.1 DNA Methylation......Page 293
11.1.1.1 CpG Islands......Page 296
11.1.2 Histone Marks......Page 297
11.1.3 Chromatin‐Regulating Enzymes......Page 298
11.1.4 Measuring DNA Methylation Levels and Histone Marks Experimentally......Page 299
11.2.1.2 Smoothing of DNA Methylation Data......Page 301
11.2.2 Differential Methylation Analysis......Page 302
11.2.3 Comethylation Analysis......Page 303
11.2.4 Working with Data on Histone Marks......Page 305
11.3.1 Measuring Chromatin States......Page 306
11.3.2 Connecting Epigenetic Marks and Gene Expression by Linear Models......Page 307
11.3.3 Markov Models and Hidden Markov Models......Page 308
11.3.4 Architecture of a Hidden Markov Model......Page 310
11.3.5 Elements of an HMM......Page 311
11.4 The Role of Epigenetics in Cellular Differentiation and Reprogramming......Page 312
11.4.2 Developmental Gene Regulatory Networks......Page 313
11.5 The Role of Epigenetics in Cancer and Complex Diseases......Page 315
11.7 Problems......Page 316
Bibliography......Page 321
12.1 Introduction......Page 323
12.2 Resources on Metabolic Network Representations......Page 326
12.3 Stoichiometric Matrix......Page 328
12.4.1 Matrices: Definitions and Notations......Page 329
12.4.2 Adding, Subtracting, and Multiplying Matrices......Page 330
12.4.4 Square Matrices and Matrix Inversion......Page 331
12.4.5 Eigenvalues of Matrices......Page 332
12.4.6 Systems of Linear Equations......Page 333
12.5 Flux Balance Analysis......Page 334
12.5.1 Gene Knockouts: MOMA Algorithm......Page 336
12.5.2 OptKnock Algorithm......Page 338
12.6 Double Description Method......Page 339
12.7.1 Steps of the Extreme Pathway Algorithm......Page 344
12.7.2 Analysis of Extreme Pathways......Page 348
12.7.3 Elementary Flux Modes......Page 349
12.7.4 Pruning Metabolic Networks: NetworkReducer......Page 351
12.8 Minimal Cut Sets......Page 352
12.8.1 Applications of Minimal Cut Sets......Page 357
12.9 High‐Flux Backbone......Page 359
12.11.1 Static Network Properties: Pathways......Page 361
Bibliography......Page 366
13.1 Biological Oscillators......Page 369
13.2 Circadian Clocks......Page 370
13.2.1 Role of Post‐transcriptional Modifications......Page 372
13.3 Ordinary Differential Equation Models......Page 373
13.3.1 Examples for ODEs......Page 374
13.4.1 Protein Synthesis and Degradation: Linear Response......Page 376
13.4.2 Phosphorylation/Dephosphorylation – Hyperbolic Response......Page 377
13.4.3 Phosphorylation/Dephosphorylation – Buzzer......Page 379
13.4.4 Perfect Adaptation – Sniffer......Page 380
13.4.5 Positive Feedback – One‐Way Switch......Page 381
13.4.7 Negative Feedback – Homeostasis......Page 382
13.4.8 Negative Feedback: Oscillatory Response......Page 384
13.4.9 Cell Cycle Control System......Page 385
13.5 Partial Differential Equations......Page 386
13.5.2 Reaction–Diffusion Systems......Page 388
13.6 Dynamic Phosphorylation of Proteins......Page 389
13.7 Summary......Page 390
13.8 Problems......Page 392
Bibliography......Page 393
14.1 Stochastic Processes......Page 395
14.1.1 Binomial Distribution......Page 396
14.1.3 Master Equation......Page 397
14.2 Dynamic Monte Carlo (Gillespie Algorithm)......Page 398
14.2.1 Basic Outline of the Gillespie Method......Page 399
14.3.1 Expression of a Single Gene......Page 400
14.3.2 Toggle Switch......Page 401
14.4.1 Model System: Bacterial Photosynthesis......Page 405
14.4.2 Pools‐and‐Proteins Model......Page 406
14.4.3 Evaluating the Binding and Unbinding Kinetics......Page 407
14.4.5 Steady‐State Regimes of the Vesicle......Page 409
14.5 Parameter Optimization with Genetic Algorithm......Page 412
14.6 Protein–Protein Association......Page 415
14.7 Brownian Dynamics Simulations......Page 416
14.8 Summary......Page 418
14.9.1 Dynamic Simulations of Networks......Page 420
Bibliography......Page 427
Chapter 15 Integrated Cellular Networks......Page 429
15.1 Response of Gene Regulatory Network to Outside Stimuli......Page 430
15.2 Whole‐Cell Model of Mycoplasma genitalium......Page 432
15.4 Integrative Differential Gene Regulatory Network for Breast Cancer Identified Putative Cancer Driver Genes......Page 436
15.5 Particle Simulations......Page 441
15.6 Summary......Page 443
Bibliography......Page 444
Chapter 16 Outlook......Page 447
Index......Page 449