Principles of Computational Cell Biology

Volkhard Helms, PhD is a full professor of bioinformatics at Saarland University. He has authored more than 100 scientific publications and received the EMBO Young Investigator Award in 2001.

• Preface of the First Edition xvPreface of the Second Edition xvii1 Networks in Biological Cells 11.1 Some Basics About Networks 11.1.1 Random Networks 21.1.2 Small-World Phenomenon 21.1.3 Scale-Free Networks 31.2 Biological Background 41.2.1 Transcriptional Regulation 51.2.2 Cellular Components 51.2.3 Spatial Organization of Eukaryotic Cells into Compartments 71.2.4 Considered Organisms 81.3 Cellular Pathways 81.3.1 Biochemical Pathways 81.3.2 Enzymatic Reactions 111.3.3 Signal Transduction 111.3.4 Cell Cycle 121.4 Ontologies and Databases 121.4.1 Ontologies 121.4.2 Gene Ontology 131.4.3 Kyoto Encyclopedia of Genes and Genomes 131.4.4 Reactome 131.4.5 Brenda 141.4.6 DAVID 141.4.7 Protein Data Bank 151.4.8 Systems Biology Markup Language 151.5 Methods for Cellular Modeling 171.6 Summary 171.7 Problems 17Bibliography 182 Structures of Protein Complexes and Subcellular Structures 212.1 Examples of Protein Complexes 222.1.1 Principles of Protein–Protein Interactions 242.1.2 Categories of Protein Complexes 272.2 Complexome: The Ensemble of Protein Complexes 282.2.1 Complexome of Saccharomyces cerevisiae 282.2.2 Bacterial Protein Complexomes 302.2.3 Complexome of Human 312.3 Experimental Determination of Three-Dimensional Structures of Protein Complexes 312.3.1 X-ray Crystallography 322.3.2 NMR 342.3.3 Electron Crystallography/Electron Microscopy 342.3.4 Cryo-EM 342.3.5 Immunoelectron Microscopy 352.3.6 Fluorescence Resonance Energy Transfer 352.3.7 Mass Spectroscopy 362.4 Density Fitting 382.4.1 Correlation-Based Density Fitting 382.5 Fourier Transformation 402.5.1 Fourier Series 402.5.2 Continuous Fourier Transform 412.5.3 Discrete Fourier Transform 412.5.4 Convolution Theorem 412.5.5 Fast Fourier Transformation 422.6 Advanced Density Fitting 442.6.1 Laplacian Filter 452.7 FFT Protein–Protein Docking 462.8 Protein–Protein Docking Using Geometric Hashing 482.9 Prediction of Assemblies from Pairwise Docking 492.9.1 CombDock 492.9.2 Multi-LZerD 522.9.3 3D-MOSAIC 522.10 Electron Tomography 532.10.1 Reconstruction of Phantom Cell 552.10.2 Protein Complexes in Mycoplasma pneumoniae 552.11 Summary 562.12 Problems 572.12.1 Mapping of Crystal Structures into EM Maps 57Bibliography 603 Analysis of Protein–Protein Binding 633.1 Modeling by Homology 633.2 Properties of Protein–Protein Interfaces 663.2.1 Size and Shape 663.2.2 Composition of Binding Interfaces 683.2.3 Hot Spots 693.2.4 Physicochemical Properties of Protein Interfaces 713.2.5 Predicting Binding Affinities of Protein–Protein Complexes 723.2.6 Forces Important for Biomolecular Association 733.3 Predicting Protein–Protein Interactions 753.3.1 Pairing Propensities 753.3.2 Statistical Potentials for Amino Acid Pairs 783.3.3 Conservation at Protein Interfaces 793.3.4 Correlated Mutations at Protein Interfaces 833.4 Summary 863.5 Problems 86Bibliography 864 Algorithms on Mathematical Graphs 894.1 Primer on Mathematical Graphs 894.2 A Few Words About Algorithms and Computer Programs 904.2.1 Implementation of Algorithms 914.2.2 Classes of Algorithms 924.3 Data Structures for Graphs 934.4 Dijkstra’s Algorithm 954.4.1 Description of the Algorithm 964.4.2 Pseudocode 1004.4.3 Running Time 1014.5 Minimum Spanning Tree 1014.5.1 Kruskal’s Algorithm 1024.6 Graph Drawing 1024.7 Summary 1044.8 Problems 1054.8.1 Force Directed Layout of Graphs 107Bibliography 1105 Protein–Protein Interaction Networks – Pairwise Connectivity 1115.1 Experimental High-Throughput Methods for Detecting Protein–Protein Interactions 1115.1.1 Gel Electrophoresis 1125.1.2 Two-Dimensional Gel Electrophoresis 1125.1.3 Affinity Chromatography 1135.1.4 Yeast Two-hybrid Screening 1145.1.5 Synthetic Lethality 1155.1.6 Gene Coexpression 1165.1.7 Databases for Interaction Networks 1165.1.8 Overlap of Interactions 1165.1.9 Criteria to Judge the Reliability of Interaction Data 1185.2 Bioinformatic Prediction of Protein–Protein Interactions 1205.2.1 Analysis of Gene Order 1215.2.2 Phylogenetic Profiling/Coevolutionary Profiling 1215.2.2.1 Coevolution 1225.3 Bayesian Networks for Judging the Accuracy of Interactions 1245.3.1 Bayes’Theorem 1255.3.2 Bayesian Network 1255.3.3 Application of Bayesian Networks to Protein–Protein Interaction Data 1265.3.3.1 Measurement of Reliability “Likelihood Ratio” 1275.3.3.2 Prior and Posterior Odds 1275.3.3.3 A Worked Example: Parameters of the Naïve Bayesian Network for Essentiality 1285.3.3.4 Fully Connected Experimental Network 1295.4 Protein Interaction Networks 1315.4.1 Protein Interaction Network of Saccharomyces cerevisiae 1315.4.2 Protein Interaction Network of Escherichia coli 1315.4.3 Protein Interaction Network of Human 1325.5 Protein Domain Networks 1325.6 Summary 1355.7 Problems 1365.7.1 Bayesian Analysis of (Fake) Protein Complexes 136Bibliography 1386 Protein–Protein Interaction Networks – Structural Hierarchies 1416.1 Protein Interaction Graph Networks 1416.1.1 Degree Distribution 1416.1.2 Clustering Coefficient 1436.2 Finding Cliques 1456.3 Random Graphs 1466.4 Scale-Free Graphs 1476.5 Detecting Communities in Networks 1496.5.1 Divisive Algorithms for Mapping onto Tree 1536.6 Modular Decomposition 1556.6.1 Modular Decomposition of Graphs 1576.7 Identification of Protein Complexes 1616.7.1 MCODE 1616.7.2 ClusterONE 1626.7.3 DACO 1636.7.4 Analysis of Target Gene Coexpression 1646.8 Network Growth Mechanisms 1656.9 Summary 1696.10 Problems 169Bibliography 1787 Protein–DNA Interactions 1817.1 Transcription Factors 1817.2 Transcription Factor-Binding Sites 1837.3 Experimental Detection of TFBS 1837.3.1 Electrophoretic Mobility Shift Assay 1837.3.2 DNAse Footprinting 1847.3.3 Protein-Binding Microarrays 1857.3.4 Chromatin Immunoprecipitation Assays 1877.4 Position-Specific Scoring Matrices 1877.5 Binding Free Energy Models 1897.6 Cis-Regulatory Motifs 1917.6.1 DACO Algorithm 1927.7 Relating Gene Expression to Binding of Transcription Factors 1927.8 Summary 1947.9 Problems 194Bibliography 1958 Gene Expression and Protein Synthesis 1978.1 Regulation of Gene Transcription at Promoters 1978.2 Experimental Analysis of Gene Expression 1988.2.1 Real-time Polymerase Chain Reaction 1998.2.2 Microarray Analysis 1998.2.3 RNA-seq 2018.3 Statistics Primer 2018.3.1 t-Test 2038.3.2 z-Score 2038.3.3 Fisher’s Exact Test 2038.3.4 Mann–Whitney–Wilcoxon Rank Sum Tests 2058.3.5 Kolmogorov–Smirnov Test 2068.3.6 Hypergeometric Test 2068.3.7 Multiple Testing Correction 2078.4 Preprocessing of Data 2078.4.1 Removal of Outlier Genes 2078.4.2 Quantile Normalization 2088.4.3 Log Transformation 2088.5 Differential Expression Analysis 2098.5.1 Volcano Plot 2108.5.2 SAM Analysis of Microarray Data 2108.5.3 Differential Expression Analysis of RNA-seq Data 2128.5.3.1 Negative Binomial Distribution 2138.5.3.2 DESeq 2138.6 Gene Ontology 2148.6.1 Functional Enrichment 2168.7 Similarity of GO Terms 2178.8 Translation of Proteins 2178.8.1 Transcription and Translation Dynamics 2188.9 Summary 2198.10 Problems 220Bibliography 2249 Gene Regulatory Networks 2279.1 Gene Regulatory Networks (GRNs) 2289.1.1 Gene Regulatory Network of E. coli 2289.1.2 Gene Regulatory Network of S. cerevisiae 2319.2 Graph Theoretical Models 2319.2.1 Coexpression Networks 2329.2.2 Bayesian Networks 2339.3 Dynamic Models 2349.3.1 Boolean Networks 2349.3.2 Reverse Engineering Boolean Networks 2359.3.3 Differential Equations Models 2369.4 DREAM: Dialogue on Reverse Engineering Assessment and Methods 2389.4.1 Input Function 2399.4.2 YAYG Approach in DREAM3 Contest 2409.5 Regulatory Motifs 2449.5.1 Feed-forward Loop (FFL) 2459.5.2 SIM 2459.5.3 Densely Overlapping Region (DOR) 2469.6 Algorithms on Gene Regulatory Networks 2479.6.1 Key-pathway Miner Algorithm 2479.6.2 Identifying Sets of Dominating Nodes 2489.6.3 Minimum Dominating Set 2499.6.4 Minimum Connected Dominating Set 2499.7 Summary 2509.8 Problems 251Bibliography 25410 Regulatory Noncoding RNA 25710.1 Introduction to RNAs 25710.2 Elements of RNA Interference: siRNAs and miRNAs 25910.3 miRNA Targets 26110.4 Predicting miRNA Targets 26410.5 Role of TFs and miRNAs in Gene-Regulatory Networks 26410.6 Constructing TF/miRNA Coregulatory Networks 26610.6.1 TFmiRWeb Service 26710.6.1.1 Construction of Candidate TF–miRNA–Gene FFLs 26810.6.1.2 Case Study 26910.7 Summary 270Bibliography 27011 Computational Epigenetics 27311.1 EpigeneticModifications 27311.1.1 DNA Methylation 27311.1.1.1 CpG Islands 27611.1.2 Histone Marks 27711.1.3 Chromatin-Regulating Enzymes 27811.1.4 Measuring DNA Methylation Levels and Histone Marks Experimentally 27911.2 Working with Epigenetic Data 28111.2.1 Processing of DNA Methylation Data 28111.2.1.1 Imputation of Missing Values 28111.2.1.2 Smoothing of DNA Methylation Data 28111.2.2 Differential Methylation Analysis 28211.2.3 Comethylation Analysis 28311.2.4 Working with Data on Histone Marks 28511.3 Chromatin States 28611.3.1 Measuring Chromatin States 28611.3.2 Connecting Epigenetic Marks and Gene Expression by Linear Models 28711.3.3 Markov Models and Hidden Markov Models 28811.3.4 Architecture of a Hidden Markov Model 29011.3.5 Elements of an HMM 29111.4 The Role of Epigenetics in Cellular Differentiation and Reprogramming 29211.4.1 Short History of Stem Cell Research 29311.4.2 Developmental Gene Regulatory Networks 29311.5 The Role of Epigenetics in Cancer and Complex Diseases 29511.6 Summary 29611.7 Problems 296Bibliography 30112 Metabolic Networks 30312.1 Introduction 30312.2 Resources on Metabolic Network Representations 30612.3 Stoichiometric Matrix 30812.4 Linear Algebra Primer 30912.4.1 Matrices: Definitions and Notations 30912.4.2 Adding, Subtracting, and Multiplying Matrices 31012.4.3 Linear Transformations, Ranks, and Transpose 31112.4.4 Square Matrices and Matrix Inversion 31112.4.5 Eigenvalues of Matrices 31212.4.6 Systems of Linear Equations 31312.5 Flux Balance Analysis 31412.5.1 Gene Knockouts: MOMA Algorithm 31612.5.2 OptKnock Algorithm 31812.6 Double Description Method 31912.7 Extreme Pathways and Elementary Modes 32412.7.1 Steps of the Extreme Pathway Algorithm 32412.7.2 Analysis of Extreme Pathways 32812.7.3 Elementary Flux Modes 32912.7.4 Pruning Metabolic Networks: NetworkReducer 33112.8 Minimal Cut Sets 33212.8.1 Applications of Minimal Cut Sets 33712.9 High-Flux Backbone 33912.10 Summary 34112.11 Problems 34112.11.1 Static Network Properties: Pathways 341Bibliography 34613 Kinetic Modeling of Cellular Processes 34913.1 Biological Oscillators 34913.2 Circadian Clocks 35013.2.1 Role of Post-transcriptional Modifications 35213.3 Ordinary Differential Equation Models 35313.3.1 Examples for ODEs 35413.4 Modeling Cellular Feedback Loops by ODEs 35613.4.1 Protein Synthesis and Degradation: Linear Response 35613.4.2 Phosphorylation/Dephosphorylation – Hyperbolic Response 35713.4.3 Phosphorylation/Dephosphorylation – Buzzer 35913.4.4 Perfect Adaptation – Sniffer 36013.4.5 Positive Feedback – One-Way Switch 36113.4.6 Mutual Inhibition – Toggle Switch 36213.4.7 Negative Feedback – Homeostasis 36213.4.8 Negative Feedback: Oscillatory Response 36413.4.9 Cell Cycle Control System 36513.5 Partial Differential Equations 36613.5.1 Spatial Gradients of Signaling Activities 36813.5.2 Reaction–Diffusion Systems 36813.6 Dynamic Phosphorylation of Proteins 36913.7 Summary 37013.8 Problems 372Bibliography 37314 Stochastic Processes in Biological Cells 37514.1 Stochastic Processes 37514.1.1 Binomial Distribution 37614.1.2 Poisson Process 37714.1.3 Master Equation 37714.2 Dynamic Monte Carlo (Gillespie Algorithm) 37814.2.1 Basic Outline of the Gillespie Method 37914.3 Stochastic Effects in Gene Transcription 38014.3.1 Expression of a Single Gene 38014.3.2 Toggle Switch 38114.4 Stochastic Modeling of a Small Molecular Network 38514.4.1 Model System: Bacterial Photosynthesis 38514.4.2 Pools-and-Proteins Model 38614.4.3 Evaluating the Binding and Unbinding Kinetics 38714.4.4 Pools of the Chromatophore Vesicle 38914.4.5 Steady-State Regimes of the Vesicle 38914.5 Parameter Optimization with Genetic Algorithm 39214.6 Protein–Protein Association 39514.7 Brownian Dynamics Simulations 39614.8 Summary 39814.9 Problems 40014.9.1 Dynamic Simulations of Networks 400Bibliography 40715 Integrated Cellular Networks 40915.1 Response of Gene Regulatory Network to Outside Stimuli 41015.2 Whole-Cell Model of Mycoplasma genitalium 41215.3 Architecture of the Nuclear Pore Complex 41615.4 Integrative Differential Gene Regulatory Network for Breast CancerIdentified Putative Cancer Driver Genes 41615.5 Particle Simulations 42115.6 Summary 423Bibliography 42416 Outlook 427Index 429