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

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.

Table of Contents:
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