Principles of computational cell biology : from protein complexes to cellular networks / Volkhard Helms.

Format:

Book

Author/Creator:

Helms, Volkhard.

Language:

English

Subjects (All):

Cytology--Data processing.

Cytology.

Cytology--Computer simulation.

Cell Biology.

Computational Biology.

Models, Biological.

Computer Simulation.

Medical Subjects:

Cell Biology.

Computational Biology.

Models, Biological.

Computer Simulation.

Physical Description:

xii, 277 pages : illustrations (some color) ; 24 cm

Place of Publication:

Weinheim : Wiley-VCH, [2008]

Summary:

This first textbook of its kind provides an ideal introduction to computational cell biology for students of biology and bioinformatics. In particular the text focuses on a network-based approach to the study of cellular systems. Almost 30 carefully designed study exercises offer excellent support for those preparing for exams in these subjects, and help introduce the more technical aspects of the topic while keeping maths to a minimum.

Contents:

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 Network Model 3

1.2 Biological Background 4

1.2.1 Cellular Components 6

1.2.2 Spatial Organization of Eukaryotic Cells - Compartments 7

1.2.3 Cellular Organisms 7

1.3 Cellular Pathways 7

1.3.1 Biochemical Pathways 7

1.3.2 Enzymatic Reactions 8

1.3.3 Signal Transduction 11

1.3.4 Cell Cycle 11

1.4 Ontologies and Databases 12

1.4.1 Ontologies 12

1.4.2 Systems Biology Markup Language 12

1.4.3 KEGG 13

1.4.4 Brenda 13

1.5 Methods in Cellular Modeling 14

2 Algorithms on Mathematical Graphs 17

2.1 Primer on Mathematical Graphs 17

2.2 A Few Words about Algorithms and Computer Programs 18

2.2.1 Implementation of Algorithms 19

2.2.2 Classes of Algorithms 20

2.3 Data Structures for Graphs 21

2.4 Dijkstra's Algorithm 23

2.4.1 Description of the Algorithm 25

2.4.2 Pseudocode 27

2.4.3 Running Time 29

2.5 Minimum Spanning Tree 29

2.5.1 Kruskal's Algorithm 31

2.6 Graph Drawing 31

3 Protein-Protein Interaction Networks - Pairwise Connectivity 39

3.1 Principles of Protein-Protein Interactions 39

3.2 Experimental High-Throughput Methods for Detecting Protein-Protein Interactions 40

3.2.1 Gel Electrophoresis 41

3.2.2 Two-Dimensional Gel Electrophoresis 41

3.2.3 Affinity Chromatography 42

3.2.4 Yeast Two-Hybrid Screening 42

3.2.5 Synthetic Lethality 44

3.2.6 Gene Coexpression 44

3.2.7 Mass Spectroscopy 44

3.2.8 Databases for Interaction Networks 44

3.2.9 Overlap of Interactions 45

3.2.10 Criteria to Judge the Reliability of Interaction Data 47

3.2.11 How Many Protein-Protein Interactions can be Expected in Yeast? 48

3.3 Bioinformatic Prediction of Protein-Protein Interactions 49

3.3.1 Analysis of Gene Order 49

3.3.2 Phylogenetic Profiling/Coevolutionary Profiling 50

3.3.3 Coevolution 51

3.4 Bayesian Networks for Judging the Accuracy of Interactions 52

3.4.1 Bayes' Theorem 53

3.4.2 Bayesian Network 54

3.4.3 Application of Bayesian Networks to Protein-Protein Interaction Data 55

3.4.3.1 Measurement of reliability "likelihood ratio" 55

3.4.3.2 Prior and posterior odds 56

3.4.3.3 A worked example: parameters of the naive Bayesian network for essentiality 57

3.4.3.4 Fully connected experimental network 57

3.5 Protein Domain Networks 59

4 Protein-Protein Interaction Networks - Structural Hierarchies 67

4.1 Protein Interaction Graph Networks 67

4.1.1 Degree Distribution 68

4.1.2 Clustering Coefficient 69

4.2 Finding Cliques 71

4.3 Random Graphs 72

4.4 Scale-Free Graphs 73

4.5 Detecting Communities in Networks 75

4.5.1 Divisive Algorithms for Mapping onto Tree 78

4.6 Modular Decomposition 82

4.6.1 Modular Decomposition of Graphs 82

4.7 Network Growth Mechanisms 86

5 Gene Regulatory Networks 99

5.1 Regulation of Gene Transcription at Promoters 100

5.2 Gene Regulatory Networks 101

5.2.1 Gene Regulatory Network of E. coli 101

5.3 Graph Theoretical Models 105

5.3.1 Coexpression Networks 105

5.3.2 Bayesian Networks 106

5.4 Dynamic Models 106

5.4.1 Boolean Networks 106

5.4.2 Reverse Engineering Boolean Networks 107

5.4.3 Differential Equations Models 110

5.5 Motifs 111

5.5.1 Feed-Forward Loop (FFL) 112

5.5.2 SIM Motif 112

5.5.3 Densely Overlapping Region (DOR) 112

6 Metabolic Networks 115

6.2 Stoichiometric Matrix 118

6.3 Linear Algebra Primer 121

6.3.1 Matrices: Definitions and Notations 121

6.3.2 Adding, Subtracting and Multiplying Matrices 121

6.3.3 Linear Transformations, Ranks and Transpose 122

6.3.4 Square Matrices and Matrix Inversion 123

6.3.5 Eigenvalues of Matrices 124

6.3.6 System of Linear Equations 124

6.4 Flux Balance Analysis 125

6.5 Double Description Method 128

6.6 Extreme Pathways and Elementary Modes 133

6.6.1 Analysis of Eextreme Pathways 137

6.6.2 Elementary Flux Modes 139

6.7 Minimal Cut Sets 140

6.7.1 Applications of Minimal Cut Sets 144

6.8 High-Flux Backbone 146

7 Kinetic Modeling of Cellular Processes 155

7.1 Ordinary Differential Equation Models 155

7.1.1 Examples for ODEs 156

7.2 Modeling Cellular Feedback Loops by ODEs 158

7.2.1 Protein Synthesis and Degradation: Linear Response 159

7.2.2 Phosphorylation/Dephosphorylation - Hyperbolic Response 160

7.2.3 Phosphorylation/Dephosphorylation - Buzzer 162

7.2.4 Perfect Adaptation - Sniffer 163

7.2.5 Positive Feedback - One-Way Switch 164

7.2.6 Mutual Inhibition - Toggle Switch 165

7.2.7 Negative Feedback - Homeostasis 166

7.2.8 Negative Feedback: Oscillatory Response 166

7.2.9 Cell Cycle Control System 167

7.3 Partial Differential Equations 169

7.3.1 Spatial Gradients of Signaling Activities 170

7.4 Dynamic Monte Carlo (Gillespie Algorithm) 172

7.4.1 Basic Outline of the Gillespie Method 173

7.5 Stochastic Modeling of a Small Molecular Network 173

7.5.1 Model System: Bacterial Photosynthesis 174

7.5.2 Pools-and-Proteins Model 176

7.5.3 Evaluating the Binding and Unbinding Kinetics 177

7.5.4 Pools of the Chromatophore Vesicle 178

7.5.5 Results for the Steady-State Regimes of the Vesicle 179

7.6 Parameter Optimization with Genetic Algorithms 182

8 Structures of Protein Complexes and Subcellular Structures 193

8.1 Examples of Protein Complexes 193

8.2 Complexeome of S. cerevisiae 197

8.3 Experimental Determination of Three-dimensional Structures of Protein Complexes 199

8.3.1 X-ray Crystallography 199

8.3.2 NMR 200

8.3.3 Electron Crystallography/Electron Microscopy 201

8.3.4 Immuno-electron Microscopy 201

8.3.5 Fluorescence Resonance Energy Transfer 202

8.4 Density Fitting 204

8.4.1 Correlation-based Fitting 204

8.5 Fourier Transformation 206

8.5.1 Fourier Series 206

8.5.2 Continuous Fourier Transform 207

8.5.3 Discrete Fourier Transform 207

8.5.4 Convolution Theorem 208

8.5.5 Fast Fourier Transformation 208

8.6 Advanced Density Fitting 210

8.6.1 Laplacian Filter 211

8.6.2 Fitting Using Core Downweighting 212

8.6.3 Core-weighted Correlation Function 214

8.6.4 Surface Overlap Maximization (SOM) 215

8.7 FFT Protein-Protein Docking 216

8.8 Prediction of Assemblies from Pairwise Docking 218

8.9 Electron Tomography 221

8.9.1 Reconstruction of a Phantom Cell 222

9 Biomolecular Association and Binding 231

9.1 Modeling by Homology 231

9.2 Structural Properties of Protein-Protein Interfaces 233

9.2.1 Size and Shape 233

9.2.2 Hot Spots 235

9.2.3 An Experimental Model System: Human Growth Hormone and its Receptor 236

9.3 Bioinformatic Prediction of Protein-Protein Interfaces 239

9.3.1 Amino acid Composition of Protein Interfaces 239

9.3.2 Pairing Propensities 240

9.3.3 Interface Statistical Potentials 240

9.3.4 Conservation at Protein Interfaces 241

9.3.5 Correlated Mutations at Protein Interfaces 243

9.3.6 Classification of Protein Interfaces 245

9.4 Forces Important for Biomolecular Association 246

9.5 Protein-Protein Association 249

9.5.1 Brownian Dynamics Simulations 250

9.6 Assembly of Macromolecular Complexes: the Ribosome 254

10 Integrated Networks 261

10.1 Correlating Interactome and Gene Regulation 261

10.2 Response of Gene Regulatory Network to Outside Stimuli 263

10.3 Integrated Analysis of Metabolic and Regulatory Networks 266

11 Outlook 271.

Notes:

Includes bibliographical references and index.

ISBN:

3527315551

9783527315550

OCLC:

233007549