Cover	1
	Title Page	5
	Copyright	6
	Contents	7
	Preface of the First Edition	17
	Preface of the Second Edition	19
	Chapter 1 Networks in Biological Cells	21
	1.1 Some Basics About Networks	21
	1.1.1 Random Networks	22
	1.1.2 Small?World Phenomenon	22
	1.1.3 Scale?Free Networks	23
	1.2 Biological Background	24
	1.2.1 Transcriptional Regulation	25
	1.2.2 Cellular Components	25
	1.2.3 Spatial Organization of Eukaryotic Cells into Compartments	27
	1.2.4 Considered Organisms	28
	1.3 Cellular Pathways	28
	1.3.1 Biochemical Pathways	28
	1.3.2 Enzymatic Reactions	31
	1.3.3 Signal Transduction	31
	1.3.4 Cell Cycle	32
	1.4 Ontologies and Databases	32
	1.4.1 Ontologies	32
	1.4.2 Gene Ontology	33
	1.4.3 Kyoto Encyclopedia of Genes and Genomes	33
	1.4.4 Reactome	33
	1.4.5 Brenda	34
	1.4.6 DAVID	34
	1.4.7 Protein Data Bank	35
	1.4.8 Systems Biology Markup Language	35
	1.5 Methods for Cellular Modeling	37
	1.6 Summary	37
	1.7 Problems	37
	Bibliography	38
	Chapter 2 Structures of Protein Complexes and Subcellular Structures	41
	2.1 Examples of Protein Complexes	42
	2.1.1 Principles of Protein–Protein Interactions	44
	2.1.2 Categories of Protein Complexes	47
	2.2 Complexome: The Ensemble of Protein Complexes	48
	2.2.1 Complexome of Saccharomyces cerevisiae	48
	2.2.2 Bacterial Protein Complexomes	50
	2.2.3 Complexome of Human	51
	2.3 Experimental Determination of Three?Dimensional Structures of Protein Complexes	51
	2.3.1 X?ray Crystallography	52
	2.3.2 NMR	54
	2.3.3 Electron Crystallography/Electron Microscopy	54
	2.3.4 Cryo?EM	54
	2.3.5 Immunoelectron Microscopy	55
	2.3.6 Fluorescence Resonance Energy Transfer	55
	2.3.7 Mass Spectroscopy	56
	2.4 Density Fitting	58
	2.4.1 Correlation?Based Density Fitting	58
	2.5 Fourier Transformation	60
	2.5.1 Fourier Series	60
	2.5.2 Continuous Fourier Transform	61
	2.5.3 Discrete Fourier Transform	61
	2.5.4 Convolution Theorem	61
	2.5.5 Fast Fourier Transformation	62
	2.6 Advanced Density Fitting	64
	2.6.1 Laplacian Filter	65
	2.7 FFT Protein–Protein Docking	66
	2.8 Protein–Protein Docking Using Geometric Hashing	68
	2.9 Prediction of Assemblies from Pairwise Docking	69
	2.9.1 CombDock	69
	2.9.2 Multi?LZerD	72
	2.9.3 3D?MOSAIC	72
	2.10 Electron Tomography	73
	2.10.1 Reconstruction of Phantom Cell	75
	2.10.2 Protein Complexes in Mycoplasma pneumoniae	75
	2.11 Summary	76
	2.12 Problems	77
	2.12.1 Mapping of Crystal Structures into EM Maps	77
	Bibliography	80
	Chapter 3 Analysis of Protein–Protein Binding	83
	3.1 Modeling by Homology	83
	3.2 Properties of Protein–Protein Interfaces	86
	3.2.1 Size and Shape	86
	3.2.2 Composition of Binding Interfaces	88
	3.2.3 Hot Spots	89
	3.2.4 Physicochemical Properties of Protein Interfaces	91
	3.2.5 Predicting Binding Affinities of Protein–Protein Complexes	92
	3.2.6 Forces Important for Biomolecular Association	93
	3.3 Predicting Protein–Protein Interactions	95
	3.3.1 Pairing Propensities	95
	3.3.2 Statistical Potentials for Amino Acid Pairs	98
	3.3.3 Conservation at Protein Interfaces	99
	3.3.4 Correlated Mutations at Protein Interfaces	103
	3.4 Summary	106
	3.5 Problems	106
	Bibliography	106
	Chapter 4 Algorithms on Mathematical Graphs	109
	4.1 Primer on Mathematical Graphs	109
	4.2 A Few Words About Algorithms and Computer Programs	110
	4.2.1 Implementation of Algorithms	111
	4.2.2 Classes of Algorithms	112
	4.3 Data Structures for Graphs	113
	4.4 Dijkstra's Algorithm	115
	4.4.1 Description of the Algorithm	116
	4.4.2 Pseudocode	120
	4.4.3 Running Time	121
	4.5 Minimum Spanning Tree	121
	4.5.1 Kruskal's Algorithm	122
	4.6 Graph Drawing	122
	4.7 Summary	124
	4.8 Problems	125
	4.8.1 Force Directed Layout of Graphs	127
	Bibliography	130
	Chapter 5 Protein–Protein Interaction Networks – Pairwise Connectivity	131
	5.1 Experimental High?Throughput Methods for Detecting Protein–Protein Interactions	131
	5.1.1 Gel Electrophoresis	132
	5.1.2 Two?Dimensional Gel Electrophoresis	132
	5.1.3 Affinity Chromatography	133
	5.1.4 Yeast Two?hybrid Screening	134
	5.1.5 Synthetic Lethality	135
	5.1.6 Gene Coexpression	136
	5.1.7 Databases for Interaction Networks	136
	5.1.8 Overlap of Interactions	136
	5.1.9 Criteria to Judge the Reliability of Interaction Data	138
	5.2 Bioinformatic Prediction of Protein–Protein Interactions	140
	5.2.1 Analysis of Gene Order	141
	5.2.2 Phylogenetic Profiling/Coevolutionary Profiling	141
	5.2.2.1 Coevolution	142
	5.3 Bayesian Networks for Judging the Accuracy of Interactions	144
	5.3.1 Bayes' Theorem	145
	5.3.2 Bayesian Network	145
	5.3.3 Application of Bayesian Networks to Protein–Protein Interaction Data	146
	5.3.3.1 Measurement of Reliability “Likelihood Ratio”	147
	5.3.3.2 Prior and Posterior Odds	147
	5.3.3.3 A Worked Example: Parameters of the Naïve Bayesian Network for Essentiality	148
	5.3.3.4 Fully Connected Experimental Network	149
	5.4 Protein Interaction Networks	151
	5.4.1 Protein Interaction Network of Saccharomyces cerevisiae	151
	5.4.2 Protein Interaction Network of Escherichia coli	151
	5.4.3 Protein Interaction Network of Human	152
	5.5 Protein Domain Networks	152
	5.6 Summary	155
	5.7 Problems	156
	5.7.1 Bayesian Analysis of (Fake) Protein Complexes	156
	Bibliography	158
	Chapter 6 Protein–Protein Interaction Networks – Structural Hierarchies	161
	6.1 Protein Interaction Graph Networks	161
	6.1.1 Degree Distribution	161
	6.1.2 Clustering Coefficient	163
	6.2 Finding Cliques	165
	6.3 Random Graphs	166
	6.4 Scale?Free Graphs	167
	6.5 Detecting Communities in Networks	169
	6.5.1 Divisive Algorithms for Mapping onto Tree	173
	6.6 Modular Decomposition	175
	6.6.1 Modular Decomposition of Graphs	177
	6.7 Identification of Protein Complexes	181
	6.7.1 MCODE	181
	6.7.2 ClusterONE	182
	6.7.3 DACO	183
	6.7.4 Analysis of Target Gene Coexpression	184
	6.8 Network Growth Mechanisms	185
	6.9 Summary	189
	6.10 Problems	189
	Bibliography	198
	Chapter 7 Protein–DNA Interactions	201
	7.1 Transcription Factors	201
	7.2 Transcription Factor?Binding Sites	203
	7.3 Experimental Detection of TFBS	203
	7.3.1 Electrophoretic Mobility Shift Assay	203
	7.3.2 DNAse Footprinting	204
	7.3.3 Protein?Binding Microarrays	205
	7.3.4 Chromatin Immunoprecipitation Assays	207
	7.4 Position?Specific Scoring Matrices	207
	7.5 Binding Free Energy Models	209
	7.6 Cis?Regulatory Motifs	211
	7.6.1 DACO Algorithm	212
	7.7 Relating Gene Expression to Binding of Transcription Factors	212
	7.8 Summary	214
	7.9 Problems	214
	Bibliography	215
	Chapter 8 Gene Expression and Protein Synthesis	217
	8.1 Regulation of Gene Transcription at Promoters	217
	8.2 Experimental Analysis of Gene Expression	218
	8.2.1 Real?time Polymerase Chain Reaction	219
	8.2.2 Microarray Analysis	219
	8.2.3 RNA?seq	221
	8.3 Statistics Primer	221
	8.3.1 t?Test	223
	8.3.2 z?Score	223
	8.3.3 Fisher's Exact Test	223
	8.3.4 Mann–Whitney–Wilcoxon Rank Sum Tests	225
	8.3.5 Kolmogorov–Smirnov Test	226
	8.3.6 Hypergeometric Test	226
	8.3.7 Multiple Testing Correction	227
	8.4 Preprocessing of Data	227
	8.4.1 Removal of Outlier Genes	227
	8.4.2 Quantile Normalization	228
	8.4.3 Log Transformation	228
	8.5 Differential Expression Analysis	229
	8.5.1 Volcano Plot	230
	8.5.2 SAM Analysis of Microarray Data	230
	8.5.3 Differential Expression Analysis of RNA?seq Data	232
	8.5.3.1 Negative Binomial Distribution	233
	8.5.3.2 DESeq	233
	8.6 Gene Ontology	234
	8.6.1 Functional Enrichment	236
	8.7 Similarity of GO Terms	237
	8.8 Translation of Proteins	237
	8.8.1 Transcription and Translation Dynamics	238
	8.9 Summary	239
	8.10 Problems	240
	Bibliography	244
	Chapter 9 Gene Regulatory Networks	247
	9.1 Gene Regulatory Networks (GRNs)	248
	9.1.1 Gene Regulatory Network of E. coli	248
	9.1.2 Gene Regulatory Network of S. cerevisiae	251
	9.2 Graph Theoretical Models	251
	9.2.1 Coexpression Networks	252
	9.2.2 Bayesian Networks	253
	9.3 Dynamic Models	254
	9.3.1 Boolean Networks	254
	9.3.2 Reverse Engineering Boolean Networks	255
	9.3.3 Differential Equations Models	256
	9.4 DREAM: Dialogue on Reverse Engineering Assessment and Methods	258
	9.4.1 Input Function	259
	9.4.2 YAYG Approach in DREAM3 Contest	260
	9.5 Regulatory Motifs	264
	9.5.1 Feed?forward Loop (FFL)	265
	9.5.2 SIM	265
	9.5.3 Densely Overlapping Region (DOR)	266
	9.6 Algorithms on Gene Regulatory Networks	267
	9.6.1 Key?pathway Miner Algorithm	267
	9.6.2 Identifying Sets of Dominating Nodes	268
	9.6.3 Minimum Dominating Set	269
	9.6.4 Minimum Connected Dominating Set	269
	9.7 Summary	270
	9.8 Problems	271
	Bibliography	274
	Chapter 10 Regulatory Noncoding RNA	277
	10.1 Introduction to RNAs	277
	10.2 Elements of RNA Interference: siRNAs and miRNAs	279
	10.3 miRNA Targets	281
	10.4 Predicting miRNA Targets	284
	10.5 Role of TFs and miRNAs in Gene?Regulatory Networks	284
	10.6 Constructing TF/miRNA Coregulatory Networks	286
	10.6.1 TFmiR Web Service	287
	10.6.1.1 Construction of Candidate TF–miRNA–Gene FFLs	288
	10.6.1.2 Case Study	289
	10.7 Summary	290
	Bibliography	290
	Chapter 11 Computational Epigenetics	293
	11.1 Epigenetic Modifications	293
	11.1.1 DNA Methylation	293
	11.1.1.1 CpG Islands	296
	11.1.2 Histone Marks	297
	11.1.3 Chromatin?Regulating Enzymes	298
	11.1.4 Measuring DNA Methylation Levels and Histone Marks Experimentally	299
	11.2 Working with Epigenetic Data	301
	11.2.1 Processing of DNA Methylation Data	301
	11.2.1.1 Imputation of Missing Values	301
	11.2.1.2 Smoothing of DNA Methylation Data	301
	11.2.2 Differential Methylation Analysis	302
	11.2.3 Comethylation Analysis	303
	11.2.4 Working with Data on Histone Marks	305
	11.3 Chromatin States	306
	11.3.1 Measuring Chromatin States	306
	11.3.2 Connecting Epigenetic Marks and Gene Expression by Linear Models	307
	11.3.3 Markov Models and Hidden Markov Models	308
	11.3.4 Architecture of a Hidden Markov Model	310
	11.3.5 Elements of an HMM	311
	11.4 The Role of Epigenetics in Cellular Differentiation and Reprogramming	312
	11.4.1 Short History of Stem Cell Research	313
	11.4.2 Developmental Gene Regulatory Networks	313
	11.5 The Role of Epigenetics in Cancer and Complex Diseases	315
	11.6 Summary	316
	11.7 Problems	316
	Bibliography	321
	Chapter 12 Metabolic Networks	323
	12.1 Introduction	323
	12.2 Resources on Metabolic Network Representations	326
	12.3 Stoichiometric Matrix	328
	12.4 Linear Algebra Primer	329
	12.4.1 Matrices: Definitions and Notations	329
	12.4.2 Adding, Subtracting, and Multiplying Matrices	330
	12.4.3 Linear Transformations, Ranks, and Transpose	331
	12.4.4 Square Matrices and Matrix Inversion	331
	12.4.5 Eigenvalues of Matrices	332
	12.4.6 Systems of Linear Equations	333
	12.5 Flux Balance Analysis	334
	12.5.1 Gene Knockouts: MOMA Algorithm	336
	12.5.2 OptKnock Algorithm	338
	12.6 Double Description Method	339
	12.7 Extreme Pathways and Elementary Modes	344
	12.7.1 Steps of the Extreme Pathway Algorithm	344
	12.7.2 Analysis of Extreme Pathways	348
	12.7.3 Elementary Flux Modes	349
	12.7.4 Pruning Metabolic Networks: NetworkReducer	351
	12.8 Minimal Cut Sets	352
	12.8.1 Applications of Minimal Cut Sets	357
	12.9 High?Flux Backbone	359
	12.10 Summary	361
	12.11 Problems	361
	12.11.1 Static Network Properties: Pathways	361
	Bibliography	366
	Chapter 13 Kinetic Modeling of Cellular Processes	369
	13.1 Biological Oscillators	369
	13.2 Circadian Clocks	370
	13.2.1 Role of Post?transcriptional Modifications	372
	13.3 Ordinary Differential Equation Models	373
	13.3.1 Examples for ODEs	374
	13.4 Modeling Cellular Feedback Loops by ODEs	376
	13.4.1 Protein Synthesis and Degradation: Linear Response	376
	13.4.2 Phosphorylation/Dephosphorylation – Hyperbolic Response	377
	13.4.3 Phosphorylation/Dephosphorylation – Buzzer	379
	13.4.4 Perfect Adaptation – Sniffer	380
	13.4.5 Positive Feedback – One?Way Switch	381
	13.4.6 Mutual Inhibition – Toggle Switch	382
	13.4.7 Negative Feedback – Homeostasis	382
	13.4.8 Negative Feedback: Oscillatory Response	384
	13.4.9 Cell Cycle Control System	385
	13.5 Partial Differential Equations	386
	13.5.1 Spatial Gradients of Signaling Activities	388
	13.5.2 Reaction–Diffusion Systems	388
	13.6 Dynamic Phosphorylation of Proteins	389
	13.7 Summary	390
	13.8 Problems	392
	Bibliography	393
	Chapter 14 Stochastic Processes in Biological Cells	395
	14.1 Stochastic Processes	395
	14.1.1 Binomial Distribution	396
	14.1.2 Poisson Process	397
	14.1.3 Master Equation	397
	14.2 Dynamic Monte Carlo (Gillespie Algorithm)	398
	14.2.1 Basic Outline of the Gillespie Method	399
	14.3 Stochastic Effects in Gene Transcription	400
	14.3.1 Expression of a Single Gene	400
	14.3.2 Toggle Switch	401
	14.4 Stochastic Modeling of a Small Molecular Network	405
	14.4.1 Model System: Bacterial Photosynthesis	405
	14.4.2 Pools?and?Proteins Model	406
	14.4.3 Evaluating the Binding and Unbinding Kinetics	407
	14.4.4 Pools of the Chromatophore Vesicle	409
	14.4.5 Steady?State Regimes of the Vesicle	409
	14.5 Parameter Optimization with Genetic Algorithm	412
	14.6 Protein–Protein Association	415
	14.7 Brownian Dynamics Simulations	416
	14.8 Summary	418
	14.9 Problems	420
	14.9.1 Dynamic Simulations of Networks	420
	Bibliography	427
	Chapter 15 Integrated Cellular Networks	429
	15.1 Response of Gene Regulatory Network to Outside Stimuli	430
	15.2 Whole?Cell Model of Mycoplasma genitalium	432
	15.3 Architecture of the Nuclear Pore Complex	436
	15.4 Integrative Differential Gene Regulatory Network for Breast Cancer Identified Putative Cancer Driver Genes	436
	15.5 Particle Simulations	441
	15.6 Summary	443
	Bibliography	444
	Chapter 16 Outlook	447
	Index	449