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Principles of Computational Cell Biology: From Protein Complexes to Cellular Networks, 2nd Edition

Principles of Computational Cell Biology: From Protein Complexes to Cellular Networks, 2nd Edition

Volkhard Helms

ISBN: 978-3-527-81033-8 December 2018 375 Pages

 E-Book

$92.99

Description

Computational cell biology courses are increasingly obligatory for biology students around the world but of course also a must for mathematics and informatics students specializing in bioinformatics. This book, now in its second edition is geared towards both audiences. The author, Volkhard Helms, has, in addition to extensive teaching experience, a strong background in biology and informatics and knows exactly what the key points are in making the book accessible for students while still conveying in depth knowledge of the subject.About 50% of new content has been added for the new edition. Much more room is now given to statistical methods, and several new chapters address protein-DNA interactions, epigenetic modifications, and microRNAs.

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Preface of the First Edition xv

Preface of the Second Edition xvii

1 Networks in Biological Cells 1

1.1 Some Basics About Networks 1

1.1.1 Random Networks 2

1.1.2 Small-World Phenomenon 2

1.1.3 Scale-Free Networks 3

1.2 Biological Background 4

1.2.1 Transcriptional Regulation 5

1.2.2 Cellular Components 5

1.2.3 Spatial Organization of Eukaryotic Cells into Compartments 7

1.2.4 Considered Organisms 8

1.3 Cellular Pathways 8

1.3.1 Biochemical Pathways 8

1.3.2 Enzymatic Reactions 11

1.3.3 Signal Transduction 11

1.3.4 Cell Cycle 12

1.4 Ontologies and Databases 12

1.4.1 Ontologies 12

1.4.2 Gene Ontology 13

1.4.3 Kyoto Encyclopedia of Genes and Genomes 13

1.4.4 Reactome 13

1.4.5 Brenda 14

1.4.6 DAVID 14

1.4.7 Protein Data Bank 15

1.4.8 Systems Biology Markup Language 15

1.5 Methods for Cellular Modeling 17

1.6 Summary 17

1.7 Problems 17

Bibliography 18

2 Structures of Protein Complexes and Subcellular Structures 21

2.1 Examples of Protein Complexes 22

2.1.1 Principles of Protein–Protein Interactions 24

2.1.2 Categories of Protein Complexes 27

2.2 Complexome: The Ensemble of Protein Complexes 28

2.2.1 Complexome of Saccharomyces cerevisiae 28

2.2.2 Bacterial Protein Complexomes 30

2.2.3 Complexome of Human 31

2.3 Experimental Determination of Three-Dimensional Structures of Protein Complexes 31

2.3.1 X-ray Crystallography 32

2.3.2 NMR 34

2.3.3 Electron Crystallography/Electron Microscopy 34

2.3.4 Cryo-EM 34

2.3.5 Immunoelectron Microscopy 35

2.3.6 Fluorescence Resonance Energy Transfer 35

2.3.7 Mass Spectroscopy 36

2.4 Density Fitting 38

2.4.1 Correlation-Based Density Fitting 38

2.5 Fourier Transformation 40

2.5.1 Fourier Series 40

2.5.2 Continuous Fourier Transform 41

2.5.3 Discrete Fourier Transform 41

2.5.4 Convolution Theorem 41

2.5.5 Fast Fourier Transformation 42

2.6 Advanced Density Fitting 44

2.6.1 Laplacian Filter 45

2.7 FFT Protein–Protein Docking 46

2.8 Protein–Protein Docking Using Geometric Hashing 48

2.9 Prediction of Assemblies from Pairwise Docking 49

2.9.1 CombDock 49

2.9.2 Multi-LZerD 52

2.9.3 3D-MOSAIC 52

2.10 Electron Tomography 53

2.10.1 Reconstruction of Phantom Cell 55

2.10.2 Protein Complexes in Mycoplasma pneumoniae 55

2.11 Summary 56

2.12 Problems 57

2.12.1 Mapping of Crystal Structures into EM Maps 57

Bibliography 60

3 Analysis of Protein–Protein Binding 63

3.1 Modeling by Homology 63

3.2 Properties of Protein–Protein Interfaces 66

3.2.1 Size and Shape 66

3.2.2 Composition of Binding Interfaces 68

3.2.3 Hot Spots 69

3.2.4 Physicochemical Properties of Protein Interfaces 71

3.2.5 Predicting Binding Affinities of Protein–Protein Complexes 72

3.2.6 Forces Important for Biomolecular Association 73

3.3 Predicting Protein–Protein Interactions 75

3.3.1 Pairing Propensities 75

3.3.2 Statistical Potentials for Amino Acid Pairs 78

3.3.3 Conservation at Protein Interfaces 79

3.3.4 Correlated Mutations at Protein Interfaces 83

3.4 Summary 86

3.5 Problems 86

Bibliography 86

4 Algorithms on Mathematical Graphs 89

4.1 Primer on Mathematical Graphs 89

4.2 A Few Words About Algorithms and Computer Programs 90

4.2.1 Implementation of Algorithms 91

4.2.2 Classes of Algorithms 92

4.3 Data Structures for Graphs 93

4.4 Dijkstra’s Algorithm 95

4.4.1 Description of the Algorithm 96

4.4.2 Pseudocode 100

4.4.3 Running Time 101

4.5 Minimum Spanning Tree 101

4.5.1 Kruskal’s Algorithm 102

4.6 Graph Drawing 102

4.7 Summary 104

4.8 Problems 105

4.8.1 Force Directed Layout of Graphs 107

Bibliography 110

5 Protein–Protein Interaction Networks – Pairwise Connectivity 111

5.1 Experimental High-Throughput Methods for Detecting Protein–Protein Interactions 111

5.1.1 Gel Electrophoresis 112

5.1.2 Two-Dimensional Gel Electrophoresis 112

5.1.3 Affinity Chromatography 113

5.1.4 Yeast Two-hybrid Screening 114

5.1.5 Synthetic Lethality 115

5.1.6 Gene Coexpression 116

5.1.7 Databases for Interaction Networks 116

5.1.8 Overlap of Interactions 116

5.1.9 Criteria to Judge the Reliability of Interaction Data 118

5.2 Bioinformatic Prediction of Protein–Protein Interactions 120

5.2.1 Analysis of Gene Order 121

5.2.2 Phylogenetic Profiling/Coevolutionary Profiling 121

5.2.2.1 Coevolution 122

5.3 Bayesian Networks for Judging the Accuracy of Interactions 124

5.3.1 Bayes’Theorem 125

5.3.2 Bayesian Network 125

5.3.3 Application of Bayesian Networks to Protein–Protein Interaction Data 126

5.3.3.1 Measurement of Reliability “Likelihood Ratio” 127

5.3.3.2 Prior and Posterior Odds 127

5.3.3.3 A Worked Example: Parameters of the Naïve Bayesian Network for Essentiality 128

5.3.3.4 Fully Connected Experimental Network 129

5.4 Protein Interaction Networks 131

5.4.1 Protein Interaction Network of Saccharomyces cerevisiae 131

5.4.2 Protein Interaction Network of Escherichia coli 131

5.4.3 Protein Interaction Network of Human 132

5.5 Protein Domain Networks 132

5.6 Summary 135

5.7 Problems 136

5.7.1 Bayesian Analysis of (Fake) Protein Complexes 136

Bibliography 138

6 Protein–Protein Interaction Networks – Structural Hierarchies 141

6.1 Protein Interaction Graph Networks 141

6.1.1 Degree Distribution 141

6.1.2 Clustering Coefficient 143

6.2 Finding Cliques 145

6.3 Random Graphs 146

6.4 Scale-Free Graphs 147

6.5 Detecting Communities in Networks 149

6.5.1 Divisive Algorithms for Mapping onto Tree 153

6.6 Modular Decomposition 155

6.6.1 Modular Decomposition of Graphs 157

6.7 Identification of Protein Complexes 161

6.7.1 MCODE 161

6.7.2 ClusterONE 162

6.7.3 DACO 163

6.7.4 Analysis of Target Gene Coexpression 164

6.8 Network Growth Mechanisms 165

6.9 Summary 169

6.10 Problems 169

Bibliography 178

7 Protein–DNA Interactions 181

7.1 Transcription Factors 181

7.2 Transcription Factor-Binding Sites 183

7.3 Experimental Detection of TFBS 183

7.3.1 Electrophoretic Mobility Shift Assay 183

7.3.2 DNAse Footprinting 184

7.3.3 Protein-Binding Microarrays 185

7.3.4 Chromatin Immunoprecipitation Assays 187

7.4 Position-Specific Scoring Matrices 187

7.5 Binding Free Energy Models 189

7.6 Cis-Regulatory Motifs 191

7.6.1 DACO Algorithm 192

7.7 Relating Gene Expression to Binding of Transcription Factors 192

7.8 Summary 194

7.9 Problems 194

Bibliography 195

8 Gene Expression and Protein Synthesis 197

8.1 Regulation of Gene Transcription at Promoters 197

8.2 Experimental Analysis of Gene Expression 198

8.2.1 Real-time Polymerase Chain Reaction 199

8.2.2 Microarray Analysis 199

8.2.3 RNA-seq 201

8.3 Statistics Primer 201

8.3.1 t-Test 203

8.3.2 z-Score 203

8.3.3 Fisher’s Exact Test 203

8.3.4 Mann–Whitney–Wilcoxon Rank Sum Tests 205

8.3.5 Kolmogorov–Smirnov Test 206

8.3.6 Hypergeometric Test 206

8.3.7 Multiple Testing Correction 207

8.4 Preprocessing of Data 207

8.4.1 Removal of Outlier Genes 207

8.4.2 Quantile Normalization 208

8.4.3 Log Transformation 208

8.5 Differential Expression Analysis 209

8.5.1 Volcano Plot 210

8.5.2 SAM Analysis of Microarray Data 210

8.5.3 Differential Expression Analysis of RNA-seq Data 212

8.5.3.1 Negative Binomial Distribution 213

8.5.3.2 DESeq 213

8.6 Gene Ontology 214

8.6.1 Functional Enrichment 216

8.7 Similarity of GO Terms 217

8.8 Translation of Proteins 217

8.8.1 Transcription and Translation Dynamics 218

8.9 Summary 219

8.10 Problems 220

Bibliography 224

9 Gene Regulatory Networks 227

9.1 Gene Regulatory Networks (GRNs) 228

9.1.1 Gene Regulatory Network of E. coli 228

9.1.2 Gene Regulatory Network of S. cerevisiae 231

9.2 Graph Theoretical Models 231

9.2.1 Coexpression Networks 232

9.2.2 Bayesian Networks 233

9.3 Dynamic Models 234

9.3.1 Boolean Networks 234

9.3.2 Reverse Engineering Boolean Networks 235

9.3.3 Differential Equations Models 236

9.4 DREAM: Dialogue on Reverse Engineering Assessment and Methods 238

9.4.1 Input Function 239

9.4.2 YAYG Approach in DREAM3 Contest 240

9.5 Regulatory Motifs 244

9.5.1 Feed-forward Loop (FFL) 245

9.5.2 SIM 245

9.5.3 Densely Overlapping Region (DOR) 246

9.6 Algorithms on Gene Regulatory Networks 247

9.6.1 Key-pathway Miner Algorithm 247

9.6.2 Identifying Sets of Dominating Nodes 248

9.6.3 Minimum Dominating Set 249

9.6.4 Minimum Connected Dominating Set 249

9.7 Summary 250

9.8 Problems 251

Bibliography 254

10 Regulatory Noncoding RNA 257

10.1 Introduction to RNAs 257

10.2 Elements of RNA Interference: siRNAs and miRNAs 259

10.3 miRNA Targets 261

10.4 Predicting miRNA Targets 264

10.5 Role of TFs and miRNAs in Gene-Regulatory Networks 264

10.6 Constructing TF/miRNA Coregulatory Networks 266

10.6.1 TFmiRWeb Service 267

10.6.1.1 Construction of Candidate TF–miRNA–Gene FFLs 268

10.6.1.2 Case Study 269

10.7 Summary 270

Bibliography 270

11 Computational Epigenetics 273

11.1 EpigeneticModifications 273

11.1.1 DNA Methylation 273

11.1.1.1 CpG Islands 276

11.1.2 Histone Marks 277

11.1.3 Chromatin-Regulating Enzymes 278

11.1.4 Measuring DNA Methylation Levels and Histone Marks Experimentally 279

11.2 Working with Epigenetic Data 281

11.2.1 Processing of DNA Methylation Data 281

11.2.1.1 Imputation of Missing Values 281

11.2.1.2 Smoothing of DNA Methylation Data 281

11.2.2 Differential Methylation Analysis 282

11.2.3 Comethylation Analysis 283

11.2.4 Working with Data on Histone Marks 285

11.3 Chromatin States 286

11.3.1 Measuring Chromatin States 286

11.3.2 Connecting Epigenetic Marks and Gene Expression by Linear Models 287

11.3.3 Markov Models and Hidden Markov Models 288

11.3.4 Architecture of a Hidden Markov Model 290

11.3.5 Elements of an HMM 291

11.4 The Role of Epigenetics in Cellular Differentiation and Reprogramming 292

11.4.1 Short History of Stem Cell Research 293

11.4.2 Developmental Gene Regulatory Networks 293

11.5 The Role of Epigenetics in Cancer and Complex Diseases 295

11.6 Summary 296

11.7 Problems 296

Bibliography 301

12 Metabolic Networks 303

12.1 Introduction 303

12.2 Resources on Metabolic Network Representations 306

12.3 Stoichiometric Matrix 308

12.4 Linear Algebra Primer 309

12.4.1 Matrices: Definitions and Notations 309

12.4.2 Adding, Subtracting, and Multiplying Matrices 310

12.4.3 Linear Transformations, Ranks, and Transpose 311

12.4.4 Square Matrices and Matrix Inversion 311

12.4.5 Eigenvalues of Matrices 312

12.4.6 Systems of Linear Equations 313

12.5 Flux Balance Analysis 314

12.5.1 Gene Knockouts: MOMA Algorithm 316

12.5.2 OptKnock Algorithm 318

12.6 Double Description Method 319

12.7 Extreme Pathways and Elementary Modes 324

12.7.1 Steps of the Extreme Pathway Algorithm 324

12.7.2 Analysis of Extreme Pathways 328

12.7.3 Elementary Flux Modes 329

12.7.4 Pruning Metabolic Networks: NetworkReducer 331

12.8 Minimal Cut Sets 332

12.8.1 Applications of Minimal Cut Sets 337

12.9 High-Flux Backbone 339

12.10 Summary 341

12.11 Problems 341

12.11.1 Static Network Properties: Pathways 341

Bibliography 346

13 Kinetic Modeling of Cellular Processes 349

13.1 Biological Oscillators 349

13.2 Circadian Clocks 350

13.2.1 Role of Post-transcriptional Modifications 352

13.3 Ordinary Differential Equation Models 353

13.3.1 Examples for ODEs 354

13.4 Modeling Cellular Feedback Loops by ODEs 356

13.4.1 Protein Synthesis and Degradation: Linear Response 356

13.4.2 Phosphorylation/Dephosphorylation – Hyperbolic Response 357

13.4.3 Phosphorylation/Dephosphorylation – Buzzer 359

13.4.4 Perfect Adaptation – Sniffer 360

13.4.5 Positive Feedback – One-Way Switch 361

13.4.6 Mutual Inhibition – Toggle Switch 362

13.4.7 Negative Feedback – Homeostasis 362

13.4.8 Negative Feedback: Oscillatory Response 364

13.4.9 Cell Cycle Control System 365

13.5 Partial Differential Equations 366

13.5.1 Spatial Gradients of Signaling Activities 368

13.5.2 Reaction–Diffusion Systems 368

13.6 Dynamic Phosphorylation of Proteins 369

13.7 Summary 370

13.8 Problems 372

Bibliography 373

14 Stochastic Processes in Biological Cells 375

14.1 Stochastic Processes 375

14.1.1 Binomial Distribution 376

14.1.2 Poisson Process 377

14.1.3 Master Equation 377

14.2 Dynamic Monte Carlo (Gillespie Algorithm) 378

14.2.1 Basic Outline of the Gillespie Method 379

14.3 Stochastic Effects in Gene Transcription 380

14.3.1 Expression of a Single Gene 380

14.3.2 Toggle Switch 381

14.4 Stochastic Modeling of a Small Molecular Network 385

14.4.1 Model System: Bacterial Photosynthesis 385

14.4.2 Pools-and-Proteins Model 386

14.4.3 Evaluating the Binding and Unbinding Kinetics 387

14.4.4 Pools of the Chromatophore Vesicle 389

14.4.5 Steady-State Regimes of the Vesicle 389

14.5 Parameter Optimization with Genetic Algorithm 392

14.6 Protein–Protein Association 395

14.7 Brownian Dynamics Simulations 396

14.8 Summary 398

14.9 Problems 400

14.9.1 Dynamic Simulations of Networks 400

Bibliography 407

15 Integrated Cellular Networks 409

15.1 Response of Gene Regulatory Network to Outside Stimuli 410

15.2 Whole-Cell Model of Mycoplasma genitalium 412

15.3 Architecture of the Nuclear Pore Complex 416

15.4 Integrative Differential Gene Regulatory Network for Breast Cancer

Identified Putative Cancer Driver Genes 416

15.5 Particle Simulations 421

15.6 Summary 423

Bibliography 424

16 Outlook 427

Index 429