دانلود کتاب Modeling the 3D conformation of genomes

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Cover......Page 1
Half Title......Page 2
Series Page......Page 3
Title Page......Page 4
Copyright Page......Page 5
Contents......Page 6
Preface......Page 8
Editor......Page 12
Contributors......Page 14
1.1 Introduction......Page 18
1.2 Chromosome Conformation Capture......Page 19
1.3 3C Variants to Obtain Genome-Scale and High-Resolution Chromatin Interaction Matrices......Page 21
1.4 Insights Obtained From Chromosome Interaction Data......Page 23
1.5 Dynamics and Cell-to-Cell Variation in Chromatin Interactions......Page 25
1.6 Polymer Models for Chromosome Folding......Page 26
1.7 Mechanisms of Chromosome Folding and Nuclear Organization......Page 27
Acknowledgments......Page 28
Part 1: First-Principles Models......Page 36
2: Cédric Vaillant and Daniel Jost......Page 38
2.1 Introduction......Page 39
2.2 3D Chromatin Organization and Epigenomics......Page 41
2.3.1 Block copolymer model......Page 44
2.3.2 Simulation methods......Page 45
2.3.3 Phase diagram of the model: Towards (micro) phase separation......Page 46
2.3.4 Comparison to experiments......Page 47
2.3.5 A dynamical, out-of-equilibrium and stochastic organization......Page 50
2.4.1 The “Nano-Reactor” hypothesis......Page 52
2.4.2 Epigenomic 1D–3D positive feedback......Page 53
2.4.3 The living chromatin model......Page 54
2.4.4 Stability of one epigenomic domain......Page 56
2.4.5 Stability of antagonistic epigenomic domains......Page 58
2.4.6 Towards a quantitative model......Page 60
2.5 Discussion and Perspectives......Page 61
References......Page 63
3.1 Introduction......Page 74
3.2.2 The phase diagram of the SBS homopolymer......Page 75
3.2.3 A switch-like control of folding......Page 76
3.3.1 The mixture model of chromatin......Page 77
3.3.2 Pattern formation (TADs) in the SBS block copolymer model......Page 78
3.4.1 Molecular nature of the binding domains......Page 79
3.5. Predicting the effects of mutations on genome 3D architecture......Page 80
3.6 Conclusions......Page 81
References......Page 82
4.1 Introduction......Page 86
4.2 Physics of Loop Extrusion and Chromosome Organization......Page 90
4.2.1 Loop extrusion during mitosis......Page 91
4.2.2 Loop extrusion during interphase......Page 95
4.3 Elements of Polymer Simulations......Page 101
References......Page 105
5.1 Hi-C Experiments: Compartments, Domains And Loops......Page 114
5.2 The Transcription Factor Model: The Bridging-Induced Attraction, Protein Clusters and Nuclear Bodies......Page 116
5.3 The Transcription Factor Model: The Bridging-Induced Attraction Drives Chromosome Conformation......Page 120
5.4 The Active and Diffusive Loop Extrusion Models......Page 122
5.5 Some Consequences of the TF and LE Models......Page 125
Acknowledgments......Page 127
6.1 Introduction......Page 132
6.2 How to Introduce Torsional Rigidity into Freely Swiveling Standard Beaded Chain Models......Page 133
6.3 The Controversy about Torsional Rigidity of Chromatin Fibers......Page 136
6.4 Setting the Desired ∆Lk......Page 138
6.5 Modeling TADs as Portions of Supercoiled Chromatin Rings......Page 140
6.6 Comparison Between Experimental and Simulated Contact Maps......Page 141
6.7 Dynamic Supercoiling of Chromatin Fibers......Page 142
6.8 TADs in Chromosomes of Fission Yeast Correspond to Domains with Divergent Transcription......Page 145
6.9 Transcription-Induced Supercoiling can Drive Chromatin Loop Extrusions......Page 147
References......Page 152
7: Andrea Papale and Angelo Rosa......Page 156
7.2.1 From DNA to chromosomes......Page 157
7.2.2 Microrheology of the nucleus......Page 163
7.3.1 “Topological” origin of chromosome territories......Page 169
7.3.2 Microrheology of the nucleus......Page 175
7.4 Conclusions and Future Directions......Page 184
References......Page 185
8: Assaf Amitai and David Holcman......Page 194
8.1 Introduction......Page 195
8.2.2Statistics analysis of SPTs......Page 196
8.2.3The Rouse polymer model......Page 199
8.2.4A Rouse polymer driven by an external force applied to a single monomer......Page 200
8.2.6Other empirical estimators and statistical properties......Page 202
8.2.8Anomalous diffusion of a chromatin locus......Page 204
8.2.9The β-polymer is a generalized Rouse model......Page 206
8.2.9.1Anomalous diffusion in fractal globules......Page 207
8.2.10Fractional Brownian motion of a locus......Page 208
8.3Directed motion of chromatin: Active motion and artifacts......Page 209
8.3.1In vivo modification of chromatin mobility......Page 211
8.3.2 Is the directed motion of a break during HR an active or a passive mechanism?......Page 213
8.3.3Long-range correlations of chromatin mobility......Page 215
8.4Conclusion: What have we learned so far from analysing SPTs of chromatin?......Page 216
References......Page 217
9: Marco Gherardi, Vittore Scolari, Remus Thei Dame, and Marco Cosentino Lagomarsino......Page 224
9.2 Chromosome Folding......Page 225
9.2.1 Replication and segregation are the main determinants of genome folding at large scales......Page 227
9.2.3 Transcription defines chromatin-interaction domains......Page 228
9.3.1 Genome compaction......Page 229
9.3.2 Genome organization......Page 230
9.3.3 “Smart polymer” behavior......Page 231
9.4.1 Subdiffusion of chromosomal loci and particles......Page 232
9.4.2 Challenges to the interpretation of data......Page 233
9.4.3 New concepts and models......Page 234
9.5.1 Compaction by molecular crowding......Page 236
9.5.2 Crowding and cytoplasmic mobility......Page 237
9.5.3 Crowding as a player in cell physiology......Page 238
9.6 Conclusions......Page 239
Part 2: Data-Driven Models......Page 248
10.1 Introduction......Page 250
10.3 Restraint-Based Modeling Strategy for Structural Determination......Page 252
10.3.2 Scoring: The distance restraints......Page 253
10.3.3 Sampling of the chromatin conformational space......Page 257
10.3.4 Optimization of TADbit parameters......Page 258
10.4 Model Validation......Page 259
10.5 Model Analysis......Page 260
10.6 Limits of the TADbit Modeling Approach......Page 263
10.7 Data “Modelability”: Are 3C Data Good Enough to Obtain 3D Models?......Page 264
10.8 Conclusion......Page 265
11.1 Introduction......Page 270
11.2.1 Frequencies of pairwise chromatin–chromatin interactions......Page 273
11.2.3 Probing chromatin proximity to nuclear bodies......Page 277
11.2.4 Imaging methods for mapping the spatio-temporal organization of the nucleus......Page 278
11.3.1 Consensus structure methods......Page 280
11.3.2 Resampling methods......Page 283
11.3.3 Population-based deconvolution methods......Page 284
11.3.4 Comprehensive data integration through population-based genome structure modeling......Page 286
11.3.5 Added value from 3D structure modeling......Page 290
11.4 Conclusions......Page 293
Acknowledgments......Page 294
12: Guido Tiana and Luca Giorgetti......Page 302
12.1 Introduction......Page 303
12.2 Setting Up the Model......Page 304
12.2.1 The experimental data......Page 305
12.2.2 Geometry of the model......Page 306
12.2.3 Choice of interactions: general principles......Page 307
12.2.4 Choice of interactions: implementation......Page 308
12.3.1 The sampling algorithm......Page 310
12.3.2 Finding the interaction matrix......Page 311
12.4 Validation of the Model......Page 312
12.5 Lessons We Learned from the Model......Page 313
12.5.2 Size and shape of domains......Page 314
12.5.4 Dynamical properties......Page 316
Acknowledgments......Page 319
13.1 Introduction......Page 322
13.2 Maximum Entropy Principle as a General Framework for Data-Driven Theories......Page 324
13.3 Interpreting DNA–DNA Ligation Assays and Decoding the Structure of Chromosomes......Page 327
13.4 Learning the Principles of Chromatin Folding: The Minimal Chromatin Model......Page 332
13.5 Breaking the Code of Chromatin Folding: Using Machine Learning to Unravel the Relationship between Epigenetics and Genome Architecture......Page 337
13.6 Conclusion......Page 342
AcknowledgEments......Page 343
14: Marco Di Stefano, Jonas Paulsen, Eivind Hovig, and Cristian Micheletti......Page 348
14.1 Introduction......Page 349
14.2.1 General polymer model......Page 350
14.2.2 Initial conditioning of the model nucleus......Page 351
14.2.3 Molecular dynamics simulations......Page 352
14.2.3.1 Mapping Of The Simulation Time To Actual Time Units......Page 353
14.2.4 Statistical models to identify constraints from Hi-C datasets......Page 354
14.2.5 Steered molecular dynamics simulations......Page 356
14.3 Steered Conformations......Page 357
14.3.1 Functional insight from structural models......Page 358
14.3.2 Nuclear positioning of functional regions......Page 359
14.3.3 Model refinement and interphase-mitotic reconfiguration......Page 361
14.4 Gene Coregulation-Colocalization Study......Page 363
14.4.1 Coregulated gene pairs......Page 364
14.4.2 Colocalization of coregulated gene pairs......Page 365
14.4.3 Testing the coregulation-colocalization hypothesis: macrodomain organization of chromosome 19......Page 369
14.5 Conclusions......Page 372
Index......Page 378