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Principles of Computational Cell Biology


Principles of Computational Cell Biology

From Protein Complexes to Cellular Networks
2. Aufl.

von: Volkhard Helms

79,99 €

Verlag: Wiley-VCH
Format: EPUB
Veröffentl.: 20.12.2018
ISBN/EAN: 9783527810321
Sprache: englisch
Anzahl Seiten: 464

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

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.
<p>Preface of the First Edition xv</p> <p>Preface of the Second Edition xvii</p> <p><b>1 Networks in Biological Cells </b>1</p> <p>1.1 Some Basics About Networks 1</p> <p>1.1.1 Random Networks 2</p> <p>1.1.2 Small-World Phenomenon 2</p> <p>1.1.3 Scale-Free Networks 3</p> <p>1.2 Biological Background 4</p> <p>1.2.1 Transcriptional Regulation 5</p> <p>1.2.2 Cellular Components 5</p> <p>1.2.3 Spatial Organization of Eukaryotic Cells into Compartments 7</p> <p>1.2.4 Considered Organisms 8</p> <p>1.3 Cellular Pathways 8</p> <p>1.3.1 Biochemical Pathways 8</p> <p>1.3.2 Enzymatic Reactions 11</p> <p>1.3.3 Signal Transduction 11</p> <p>1.3.4 Cell Cycle 12</p> <p>1.4 Ontologies and Databases 12</p> <p>1.4.1 Ontologies 12</p> <p>1.4.2 Gene Ontology 13</p> <p>1.4.3 Kyoto Encyclopedia of Genes and Genomes 13</p> <p>1.4.4 Reactome 13</p> <p>1.4.5 Brenda 14</p> <p>1.4.6 DAVID 14</p> <p>1.4.7 Protein Data Bank 15</p> <p>1.4.8 Systems Biology Markup Language 15</p> <p>1.5 Methods for Cellular Modeling 17</p> <p>1.6 Summary 17</p> <p>1.7 Problems 17</p> <p>Bibliography 18</p> <p><b>2 Structures of Protein Complexes and Subcellular Structures </b>21</p> <p>2.1 Examples of Protein Complexes 22</p> <p>2.1.1 Principles of Protein–Protein Interactions 24</p> <p>2.1.2 Categories of Protein Complexes 27</p> <p>2.2 Complexome: The Ensemble of Protein Complexes 28</p> <p>2.2.1 Complexome of Saccharomyces cerevisiae 28</p> <p>2.2.2 Bacterial Protein Complexomes 30</p> <p>2.2.3 Complexome of Human 31</p> <p>2.3 Experimental Determination of Three-Dimensional Structures of Protein Complexes 31</p> <p>2.3.1 X-ray Crystallography 32</p> <p>2.3.2 NMR 34</p> <p>2.3.3 Electron Crystallography/Electron Microscopy 34</p> <p>2.3.4 Cryo-EM 34</p> <p>2.3.5 Immunoelectron Microscopy 35</p> <p>2.3.6 Fluorescence Resonance Energy Transfer 35</p> <p>2.3.7 Mass Spectroscopy 36</p> <p>2.4 Density Fitting 38</p> <p>2.4.1 Correlation-Based Density Fitting 38</p> <p>2.5 Fourier Transformation 40</p> <p>2.5.1 Fourier Series 40</p> <p>2.5.2 Continuous Fourier Transform 41</p> <p>2.5.3 Discrete Fourier Transform 41</p> <p>2.5.4 Convolution Theorem 41</p> <p>2.5.5 Fast Fourier Transformation 42</p> <p>2.6 Advanced Density Fitting 44</p> <p>2.6.1 Laplacian Filter 45</p> <p>2.7 FFT Protein–Protein Docking 46</p> <p>2.8 Protein–Protein Docking Using Geometric Hashing 48</p> <p>2.9 Prediction of Assemblies from Pairwise Docking 49</p> <p>2.9.1 CombDock 49</p> <p>2.9.2 Multi-LZerD 52</p> <p>2.9.3 3D-MOSAIC 52</p> <p>2.10 Electron Tomography 53</p> <p>2.10.1 Reconstruction of Phantom Cell 55</p> <p>2.10.2 Protein Complexes in Mycoplasma pneumoniae 55</p> <p>2.11 Summary 56</p> <p>2.12 Problems 57</p> <p>2.12.1 Mapping of Crystal Structures into EM Maps 57</p> <p>Bibliography 60</p> <p><b>3 Analysis of Protein–Protein Binding </b>63</p> <p>3.1 Modeling by Homology 63</p> <p>3.2 Properties of Protein–Protein Interfaces 66</p> <p>3.2.1 Size and Shape 66</p> <p>3.2.2 Composition of Binding Interfaces 68</p> <p>3.2.3 Hot Spots 69</p> <p>3.2.4 Physicochemical Properties of Protein Interfaces 71</p> <p>3.2.5 Predicting Binding Affinities of Protein–Protein Complexes 72</p> <p>3.2.6 Forces Important for Biomolecular Association 73</p> <p>3.3 Predicting Protein–Protein Interactions 75</p> <p>3.3.1 Pairing Propensities 75</p> <p>3.3.2 Statistical Potentials for Amino Acid Pairs 78</p> <p>3.3.3 Conservation at Protein Interfaces 79</p> <p>3.3.4 Correlated Mutations at Protein Interfaces 83</p> <p>3.4 Summary 86</p> <p>3.5 Problems 86</p> <p>Bibliography 86</p> <p><b>4 Algorithms on Mathematical Graphs </b>89</p> <p>4.1 Primer on Mathematical Graphs 89</p> <p>4.2 A Few Words About Algorithms and Computer Programs 90</p> <p>4.2.1 Implementation of Algorithms 91</p> <p>4.2.2 Classes of Algorithms 92</p> <p>4.3 Data Structures for Graphs 93</p> <p>4.4 Dijkstra’s Algorithm 95</p> <p>4.4.1 Description of the Algorithm 96</p> <p>4.4.2 Pseudocode 100</p> <p>4.4.3 Running Time 101</p> <p>4.5 Minimum Spanning Tree 101</p> <p>4.5.1 Kruskal’s Algorithm 102</p> <p>4.6 Graph Drawing 102</p> <p>4.7 Summary 104</p> <p>4.8 Problems 105</p> <p>4.8.1 Force Directed Layout of Graphs 107</p> <p>Bibliography 110</p> <p><b>5 Protein–Protein Interaction Networks – Pairwise Connectivity </b>111</p> <p>5.1 Experimental High-Throughput Methods for Detecting Protein–Protein Interactions 111</p> <p>5.1.1 Gel Electrophoresis 112</p> <p>5.1.2 Two-Dimensional Gel Electrophoresis 112</p> <p>5.1.3 Affinity Chromatography 113</p> <p>5.1.4 Yeast Two-hybrid Screening 114</p> <p>5.1.5 Synthetic Lethality 115</p> <p>5.1.6 Gene Coexpression 116</p> <p>5.1.7 Databases for Interaction Networks 116</p> <p>5.1.8 Overlap of Interactions 116</p> <p>5.1.9 Criteria to Judge the Reliability of Interaction Data 118</p> <p>5.2 Bioinformatic Prediction of Protein–Protein Interactions 120</p> <p>5.2.1 Analysis of Gene Order 121</p> <p>5.2.2 Phylogenetic Profiling/Coevolutionary Profiling 121</p> <p>5.2.2.1 Coevolution 122</p> <p>5.3 Bayesian Networks for Judging the Accuracy of Interactions 124</p> <p>5.3.1 Bayes’Theorem 125</p> <p>5.3.2 Bayesian Network 125</p> <p>5.3.3 Application of Bayesian Networks to Protein–Protein Interaction Data 126</p> <p>5.3.3.1 Measurement of Reliability “Likelihood Ratio” 127</p> <p>5.3.3.2 Prior and Posterior Odds 127</p> <p>5.3.3.3 A Worked Example: Parameters of the Naïve Bayesian Network for Essentiality 128</p> <p>5.3.3.4 Fully Connected Experimental Network 129</p> <p>5.4 Protein Interaction Networks 131</p> <p>5.4.1 Protein Interaction Network of Saccharomyces cerevisiae 131</p> <p>5.4.2 Protein Interaction Network of Escherichia coli 131</p> <p>5.4.3 Protein Interaction Network of Human 132</p> <p>5.5 Protein Domain Networks 132</p> <p>5.6 Summary 135</p> <p>5.7 Problems 136</p> <p>5.7.1 Bayesian Analysis of (Fake) Protein Complexes 136</p> <p>Bibliography 138</p> <p><b>6 Protein–Protein Interaction Networks – Structural Hierarchies </b>141</p> <p>6.1 Protein Interaction Graph Networks 141</p> <p>6.1.1 Degree Distribution 141</p> <p>6.1.2 Clustering Coefficient 143</p> <p>6.2 Finding Cliques 145</p> <p>6.3 Random Graphs 146</p> <p>6.4 Scale-Free Graphs 147</p> <p>6.5 Detecting Communities in Networks 149</p> <p>6.5.1 Divisive Algorithms for Mapping onto Tree 153</p> <p>6.6 Modular Decomposition 155</p> <p>6.6.1 Modular Decomposition of Graphs 157</p> <p>6.7 Identification of Protein Complexes 161</p> <p>6.7.1 MCODE 161</p> <p>6.7.2 ClusterONE 162</p> <p>6.7.3 DACO 163</p> <p>6.7.4 Analysis of Target Gene Coexpression 164</p> <p>6.8 Network Growth Mechanisms 165</p> <p>6.9 Summary 169</p> <p>6.10 Problems 169</p> <p>Bibliography 178</p> <p><b>7 Protein–DNA Interactions </b>181</p> <p>7.1 Transcription Factors 181</p> <p>7.2 Transcription Factor-Binding Sites 183</p> <p>7.3 Experimental Detection of TFBS 183</p> <p>7.3.1 Electrophoretic Mobility Shift Assay 183</p> <p>7.3.2 DNAse Footprinting 184</p> <p>7.3.3 Protein-Binding Microarrays 185</p> <p>7.3.4 Chromatin Immunoprecipitation Assays 187</p> <p>7.4 Position-Specific Scoring Matrices 187</p> <p>7.5 Binding Free Energy Models 189</p> <p>7.6 Cis-Regulatory Motifs 191</p> <p>7.6.1 DACO Algorithm 192</p> <p>7.7 Relating Gene Expression to Binding of Transcription Factors 192</p> <p>7.8 Summary 194</p> <p>7.9 Problems 194</p> <p>Bibliography 195</p> <p><b>8 Gene Expression and Protein Synthesis </b>197</p> <p>8.1 Regulation of Gene Transcription at Promoters 197</p> <p>8.2 Experimental Analysis of Gene Expression 198</p> <p>8.2.1 Real-time Polymerase Chain Reaction 199</p> <p>8.2.2 Microarray Analysis 199</p> <p>8.2.3 RNA-seq 201</p> <p>8.3 Statistics Primer 201</p> <p>8.3.1 t-Test 203</p> <p>8.3.2 z-Score 203</p> <p>8.3.3 Fisher’s Exact Test 203</p> <p>8.3.4 Mann–Whitney–Wilcoxon Rank Sum Tests 205</p> <p>8.3.5 Kolmogorov–Smirnov Test 206</p> <p>8.3.6 Hypergeometric Test 206</p> <p>8.3.7 Multiple Testing Correction 207</p> <p>8.4 Preprocessing of Data 207</p> <p>8.4.1 Removal of Outlier Genes 207</p> <p>8.4.2 Quantile Normalization 208</p> <p>8.4.3 Log Transformation 208</p> <p>8.5 Differential Expression Analysis 209</p> <p>8.5.1 Volcano Plot 210</p> <p>8.5.2 SAM Analysis of Microarray Data 210</p> <p>8.5.3 Differential Expression Analysis of RNA-seq Data 212</p> <p>8.5.3.1 Negative Binomial Distribution 213</p> <p>8.5.3.2 DESeq 213</p> <p>8.6 Gene Ontology 214</p> <p>8.6.1 Functional Enrichment 216</p> <p>8.7 Similarity of GO Terms 217</p> <p>8.8 Translation of Proteins 217</p> <p>8.8.1 Transcription and Translation Dynamics 218</p> <p>8.9 Summary 219</p> <p>8.10 Problems 220</p> <p>Bibliography 224</p> <p><b>9 Gene Regulatory Networks </b>227</p> <p>9.1 Gene Regulatory Networks (GRNs) 228</p> <p>9.1.1 Gene Regulatory Network of E. coli 228</p> <p>9.1.2 Gene Regulatory Network of S. cerevisiae 231</p> <p>9.2 Graph Theoretical Models 231</p> <p>9.2.1 Coexpression Networks 232</p> <p>9.2.2 Bayesian Networks 233</p> <p>9.3 Dynamic Models 234</p> <p>9.3.1 Boolean Networks 234</p> <p>9.3.2 Reverse Engineering Boolean Networks 235</p> <p>9.3.3 Differential Equations Models 236</p> <p>9.4 DREAM: Dialogue on Reverse Engineering Assessment and Methods 238</p> <p>9.4.1 Input Function 239</p> <p>9.4.2 YAYG Approach in DREAM3 Contest 240</p> <p>9.5 Regulatory Motifs 244</p> <p>9.5.1 Feed-forward Loop (FFL) 245</p> <p>9.5.2 SIM 245</p> <p>9.5.3 Densely Overlapping Region (DOR) 246</p> <p>9.6 Algorithms on Gene Regulatory Networks 247</p> <p>9.6.1 Key-pathway Miner Algorithm 247</p> <p>9.6.2 Identifying Sets of Dominating Nodes 248</p> <p>9.6.3 Minimum Dominating Set 249</p> <p>9.6.4 Minimum Connected Dominating Set 249</p> <p>9.7 Summary 250</p> <p>9.8 Problems 251</p> <p>Bibliography 254</p> <p><b>10 Regulatory Noncoding RNA </b>257</p> <p>10.1 Introduction to RNAs 257</p> <p>10.2 Elements of RNA Interference: siRNAs and miRNAs 259</p> <p>10.3 miRNA Targets 261</p> <p>10.4 Predicting miRNA Targets 264</p> <p>10.5 Role of TFs and miRNAs in Gene-Regulatory Networks 264</p> <p>10.6 Constructing TF/miRNA Coregulatory Networks 266</p> <p>10.6.1 TFmiRWeb Service 267</p> <p>10.6.1.1 Construction of Candidate TF–miRNA–Gene FFLs 268</p> <p>10.6.1.2 Case Study 269</p> <p>10.7 Summary 270</p> <p>Bibliography 270</p> <p><b>11 Computational Epigenetics </b>273</p> <p>11.1 EpigeneticModifications 273</p> <p>11.1.1 DNA Methylation 273</p> <p>11.1.1.1 CpG Islands 276</p> <p>11.1.2 Histone Marks 277</p> <p>11.1.3 Chromatin-Regulating Enzymes 278</p> <p>11.1.4 Measuring DNA Methylation Levels and Histone Marks Experimentally 279</p> <p>11.2 Working with Epigenetic Data 281</p> <p>11.2.1 Processing of DNA Methylation Data 281</p> <p>11.2.1.1 Imputation of Missing Values 281</p> <p>11.2.1.2 Smoothing of DNA Methylation Data 281</p> <p>11.2.2 Differential Methylation Analysis 282</p> <p>11.2.3 Comethylation Analysis 283</p> <p>11.2.4 Working with Data on Histone Marks 285</p> <p>11.3 Chromatin States 286</p> <p>11.3.1 Measuring Chromatin States 286</p> <p>11.3.2 Connecting Epigenetic Marks and Gene Expression by Linear Models 287</p> <p>11.3.3 Markov Models and Hidden Markov Models 288</p> <p>11.3.4 Architecture of a Hidden Markov Model 290</p> <p>11.3.5 Elements of an HMM 291</p> <p>11.4 The Role of Epigenetics in Cellular Differentiation and Reprogramming 292</p> <p>11.4.1 Short History of Stem Cell Research 293</p> <p>11.4.2 Developmental Gene Regulatory Networks 293</p> <p>11.5 The Role of Epigenetics in Cancer and Complex Diseases 295</p> <p>11.6 Summary 296</p> <p>11.7 Problems 296</p> <p>Bibliography 301</p> <p><b>12 Metabolic Networks </b>303</p> <p>12.1 Introduction 303</p> <p>12.2 Resources on Metabolic Network Representations 306</p> <p>12.3 Stoichiometric Matrix 308</p> <p>12.4 Linear Algebra Primer 309</p> <p>12.4.1 Matrices: Definitions and Notations 309</p> <p>12.4.2 Adding, Subtracting, and Multiplying Matrices 310</p> <p>12.4.3 Linear Transformations, Ranks, and Transpose 311</p> <p>12.4.4 Square Matrices and Matrix Inversion 311</p> <p>12.4.5 Eigenvalues of Matrices 312</p> <p>12.4.6 Systems of Linear Equations 313</p> <p>12.5 Flux Balance Analysis 314</p> <p>12.5.1 Gene Knockouts: MOMA Algorithm 316</p> <p>12.5.2 OptKnock Algorithm 318</p> <p>12.6 Double Description Method 319</p> <p>12.7 Extreme Pathways and Elementary Modes 324</p> <p>12.7.1 Steps of the Extreme Pathway Algorithm 324</p> <p>12.7.2 Analysis of Extreme Pathways 328</p> <p>12.7.3 Elementary Flux Modes 329</p> <p>12.7.4 Pruning Metabolic Networks: NetworkReducer 331</p> <p>12.8 Minimal Cut Sets 332</p> <p>12.8.1 Applications of Minimal Cut Sets 337</p> <p>12.9 High-Flux Backbone 339</p> <p>12.10 Summary 341</p> <p>12.11 Problems 341</p> <p>12.11.1 Static Network Properties: Pathways 341</p> <p>Bibliography 346</p> <p><b>13 Kinetic Modeling of Cellular Processes </b>349</p> <p>13.1 Biological Oscillators 349</p> <p>13.2 Circadian Clocks 350</p> <p>13.2.1 Role of Post-transcriptional Modifications 352</p> <p>13.3 Ordinary Differential Equation Models 353</p> <p>13.3.1 Examples for ODEs 354</p> <p>13.4 Modeling Cellular Feedback Loops by ODEs 356</p> <p>13.4.1 Protein Synthesis and Degradation: Linear Response 356</p> <p>13.4.2 Phosphorylation/Dephosphorylation – Hyperbolic Response 357</p> <p>13.4.3 Phosphorylation/Dephosphorylation – Buzzer 359</p> <p>13.4.4 Perfect Adaptation – Sniffer 360</p> <p>13.4.5 Positive Feedback – One-Way Switch 361</p> <p>13.4.6 Mutual Inhibition – Toggle Switch 362</p> <p>13.4.7 Negative Feedback – Homeostasis 362</p> <p>13.4.8 Negative Feedback: Oscillatory Response 364</p> <p>13.4.9 Cell Cycle Control System 365</p> <p>13.5 Partial Differential Equations 366</p> <p>13.5.1 Spatial Gradients of Signaling Activities 368</p> <p>13.5.2 Reaction–Diffusion Systems 368</p> <p>13.6 Dynamic Phosphorylation of Proteins 369</p> <p>13.7 Summary 370</p> <p>13.8 Problems 372</p> <p>Bibliography 373</p> <p><b>14 Stochastic Processes in Biological Cells </b>375</p> <p>14.1 Stochastic Processes 375</p> <p>14.1.1 Binomial Distribution 376</p> <p>14.1.2 Poisson Process 377</p> <p>14.1.3 Master Equation 377</p> <p>14.2 Dynamic Monte Carlo (Gillespie Algorithm) 378</p> <p>14.2.1 Basic Outline of the Gillespie Method 379</p> <p>14.3 Stochastic Effects in Gene Transcription 380</p> <p>14.3.1 Expression of a Single Gene 380</p> <p>14.3.2 Toggle Switch 381</p> <p>14.4 Stochastic Modeling of a Small Molecular Network 385</p> <p>14.4.1 Model System: Bacterial Photosynthesis 385</p> <p>14.4.2 Pools-and-Proteins Model 386</p> <p>14.4.3 Evaluating the Binding and Unbinding Kinetics 387</p> <p>14.4.4 Pools of the Chromatophore Vesicle 389</p> <p>14.4.5 Steady-State Regimes of the Vesicle 389</p> <p>14.5 Parameter Optimization with Genetic Algorithm 392</p> <p>14.6 Protein–Protein Association 395</p> <p>14.7 Brownian Dynamics Simulations 396</p> <p>14.8 Summary 398</p> <p>14.9 Problems 400</p> <p>14.9.1 Dynamic Simulations of Networks 400</p> <p>Bibliography 407</p> <p><b>15 Integrated Cellular Networks </b>409</p> <p>15.1 Response of Gene Regulatory Network to Outside Stimuli 410</p> <p>15.2 Whole-Cell Model of Mycoplasma genitalium 412</p> <p>15.3 Architecture of the Nuclear Pore Complex 416</p> <p>15.4 Integrative Differential Gene Regulatory Network for Breast Cancer</p> <p>Identified Putative Cancer Driver Genes 416</p> <p>15.5 Particle Simulations 421</p> <p>15.6 Summary 423</p> <p>Bibliography 424</p> <p><b>16 Outlook </b>427</p> <p>Index 429</p>
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.

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