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

Cover

Title Page

Copyright

Contents

Preface of the First Edition

Preface of the Second Edition

Chapter 1 Networks in Biological Cells

1.1 Some Basics About Networks

1.1.1 Random Networks

1.1.2 Small?World Phenomenon

1.1.3 Scale?Free Networks

1.2 Biological Background

1.2.1 Transcriptional Regulation

1.2.2 Cellular Components

1.2.3 Spatial Organization of Eukaryotic Cells into Compartments

1.2.4 Considered Organisms

1.3 Cellular Pathways

1.3.1 Biochemical Pathways

1.3.2 Enzymatic Reactions

1.3.3 Signal Transduction

1.3.4 Cell Cycle

1.4 Ontologies and Databases

1.4.1 Ontologies

1.4.2 Gene Ontology

1.4.3 Kyoto Encyclopedia of Genes and Genomes

1.4.4 Reactome

1.4.5 Brenda

1.4.6 DAVID

1.4.7 Protein Data Bank

1.4.8 Systems Biology Markup Language

1.5 Methods for Cellular Modeling

1.6 Summary

1.7 Problems

Bibliography

Chapter 2 Structures of Protein Complexes and Subcellular Structures

2.1 Examples of Protein Complexes

2.1.1 Principles of Protein–Protein Interactions

2.1.2 Categories of Protein Complexes

2.2 Complexome: The Ensemble of Protein Complexes

2.2.1 Complexome of Saccharomyces cerevisiae

2.2.2 Bacterial Protein Complexomes

2.2.3 Complexome of Human

2.3 Experimental Determination of Three?Dimensional Structures of Protein Complexes

2.3.1 X?ray Crystallography

2.3.2 NMR

2.3.3 Electron Crystallography/Electron Microscopy

2.3.4 Cryo?EM

2.3.5 Immunoelectron Microscopy

2.3.6 Fluorescence Resonance Energy Transfer

2.3.7 Mass Spectroscopy

2.4 Density Fitting

2.4.1 Correlation?Based Density Fitting

2.5 Fourier Transformation

2.5.1 Fourier Series

2.5.2 Continuous Fourier Transform

2.5.3 Discrete Fourier Transform

2.5.4 Convolution Theorem

2.5.5 Fast Fourier Transformation

2.6 Advanced Density Fitting

2.6.1 Laplacian Filter

2.7 FFT Protein–Protein Docking

2.8 Protein–Protein Docking Using Geometric Hashing

2.9 Prediction of Assemblies from Pairwise Docking

2.9.1 CombDock

2.9.2 Multi?LZerD

2.9.3 3D?MOSAIC

2.10 Electron Tomography

2.10.1 Reconstruction of Phantom Cell

2.10.2 Protein Complexes in Mycoplasma pneumoniae

2.11 Summary

2.12 Problems

2.12.1 Mapping of Crystal Structures into EM Maps

Bibliography

Chapter 3 Analysis of Protein–Protein Binding

3.1 Modeling by Homology

3.2 Properties of Protein–Protein Interfaces

3.2.1 Size and Shape

3.2.2 Composition of Binding Interfaces

3.2.3 Hot Spots

3.2.4 Physicochemical Properties of Protein Interfaces

3.2.5 Predicting Binding Affinities of Protein–Protein Complexes

3.2.6 Forces Important for Biomolecular Association

3.3 Predicting Protein–Protein Interactions

3.3.1 Pairing Propensities

3.3.2 Statistical Potentials for Amino Acid Pairs

3.3.3 Conservation at Protein Interfaces

3.3.4 Correlated Mutations at Protein Interfaces

3.4 Summary

3.5 Problems

Bibliography

Chapter 4 Algorithms on Mathematical Graphs

4.1 Primer on Mathematical Graphs

4.2 A Few Words About Algorithms and Computer Programs

4.2.1 Implementation of Algorithms

4.2.2 Classes of Algorithms

4.3 Data Structures for Graphs

4.4 Dijkstra's Algorithm

4.4.1 Description of the Algorithm

4.4.2 Pseudocode

4.4.3 Running Time

4.5 Minimum Spanning Tree

4.5.1 Kruskal's Algorithm

4.6 Graph Drawing

4.7 Summary

4.8 Problems

4.8.1 Force Directed Layout of Graphs

Bibliography

Chapter 5 Protein–Protein Interaction Networks – Pairwise Connectivity

5.1 Experimental High?Throughput Methods for Detecting Protein–Protein Interactions

5.1.1 Gel Electrophoresis

5.1.2 Two?Dimensional Gel Electrophoresis

5.1.3 Affinity Chromatography

5.1.4 Yeast Two?hybrid Screening

5.1.5 Synthetic Lethality

5.1.6 Gene Coexpression

5.1.7 Databases for Interaction Networks

5.1.8 Overlap of Interactions

5.1.9 Criteria to Judge the Reliability of Interaction Data

5.2 Bioinformatic Prediction of Protein–Protein Interactions

5.2.1 Analysis of Gene Order

5.2.2 Phylogenetic Profiling/Coevolutionary Profiling

5.2.2.1 Coevolution

5.3 Bayesian Networks for Judging the Accuracy of Interactions

5.3.1 Bayes' Theorem

5.3.2 Bayesian Network

5.3.3 Application of Bayesian Networks to Protein–Protein Interaction Data

5.3.3.1 Measurement of Reliability “Likelihood Ratio”

5.3.3.2 Prior and Posterior Odds

5.3.3.3 A Worked Example: Parameters of the Naïve Bayesian Network for Essentiality

5.3.3.4 Fully Connected Experimental Network

5.4 Protein Interaction Networks

5.4.1 Protein Interaction Network of Saccharomyces cerevisiae

5.4.2 Protein Interaction Network of Escherichia coli

5.4.3 Protein Interaction Network of Human

5.5 Protein Domain Networks

5.6 Summary

5.7 Problems

5.7.1 Bayesian Analysis of (Fake) Protein Complexes

Bibliography

Chapter 6 Protein–Protein Interaction Networks – Structural Hierarchies

6.1 Protein Interaction Graph Networks

6.1.1 Degree Distribution

6.1.2 Clustering Coefficient

6.2 Finding Cliques

6.3 Random Graphs

6.4 Scale?Free Graphs

6.5 Detecting Communities in Networks

6.5.1 Divisive Algorithms for Mapping onto Tree

6.6 Modular Decomposition

6.6.1 Modular Decomposition of Graphs

6.7 Identification of Protein Complexes

6.7.1 MCODE

6.7.2 ClusterONE

6.7.3 DACO

6.7.4 Analysis of Target Gene Coexpression

6.8 Network Growth Mechanisms

6.9 Summary

6.10 Problems

Bibliography

Chapter 7 Protein–DNA Interactions

7.1 Transcription Factors

7.2 Transcription Factor?Binding Sites

7.3 Experimental Detection of TFBS

7.3.1 Electrophoretic Mobility Shift Assay

7.3.2 DNAse Footprinting

7.3.3 Protein?Binding Microarrays

7.3.4 Chromatin Immunoprecipitation Assays

7.4 Position?Specific Scoring Matrices

7.5 Binding Free Energy Models

7.6 Cis?Regulatory Motifs

7.6.1 DACO Algorithm

7.7 Relating Gene Expression to Binding of Transcription Factors

7.8 Summary

7.9 Problems

Bibliography

Chapter 8 Gene Expression and Protein Synthesis

8.1 Regulation of Gene Transcription at Promoters

8.2 Experimental Analysis of Gene Expression

8.2.1 Real?time Polymerase Chain Reaction

8.2.2 Microarray Analysis

8.2.3 RNA?seq

8.3 Statistics Primer

8.3.1 t?Test

8.3.2 z?Score

8.3.3 Fisher's Exact Test

8.3.4 Mann–Whitney–Wilcoxon Rank Sum Tests

8.3.5 Kolmogorov–Smirnov Test

8.3.6 Hypergeometric Test

8.3.7 Multiple Testing Correction

8.4 Preprocessing of Data

8.4.1 Removal of Outlier Genes

8.4.2 Quantile Normalization

8.4.3 Log Transformation

8.5 Differential Expression Analysis

8.5.1 Volcano Plot

8.5.2 SAM Analysis of Microarray Data

8.5.3 Differential Expression Analysis of RNA?seq Data

8.5.3.1 Negative Binomial Distribution

8.5.3.2 DESeq

8.6 Gene Ontology

8.6.1 Functional Enrichment

8.7 Similarity of GO Terms

8.8 Translation of Proteins

8.8.1 Transcription and Translation Dynamics

8.9 Summary

8.10 Problems

Bibliography

Chapter 9 Gene Regulatory Networks

9.1 Gene Regulatory Networks (GRNs)

9.1.1 Gene Regulatory Network of E. coli

9.1.2 Gene Regulatory Network of S. cerevisiae

9.2 Graph Theoretical Models

9.2.1 Coexpression Networks

9.2.2 Bayesian Networks

9.3 Dynamic Models

9.3.1 Boolean Networks

9.3.2 Reverse Engineering Boolean Networks

9.3.3 Differential Equations Models

9.4 DREAM: Dialogue on Reverse Engineering Assessment and Methods

9.4.1 Input Function

9.4.2 YAYG Approach in DREAM3 Contest

9.5 Regulatory Motifs

9.5.1 Feed?forward Loop (FFL)

9.5.2 SIM

9.5.3 Densely Overlapping Region (DOR)

9.6 Algorithms on Gene Regulatory Networks

9.6.1 Key?pathway Miner Algorithm

9.6.2 Identifying Sets of Dominating Nodes

9.6.3 Minimum Dominating Set

9.6.4 Minimum Connected Dominating Set

9.7 Summary

9.8 Problems

Bibliography

Chapter 10 Regulatory Noncoding RNA

10.1 Introduction to RNAs

10.2 Elements of RNA Interference: siRNAs and miRNAs

10.3 miRNA Targets

10.4 Predicting miRNA Targets

10.5 Role of TFs and miRNAs in Gene?Regulatory Networks

10.6 Constructing TF/miRNA Coregulatory Networks

10.6.1 TFmiR Web Service

10.6.1.1 Construction of Candidate TF–miRNA–Gene FFLs

10.6.1.2 Case Study

10.7 Summary

Bibliography

Chapter 11 Computational Epigenetics

11.1 Epigenetic Modifications

11.1.1 DNA Methylation

11.1.1.1 CpG Islands

11.1.2 Histone Marks

11.1.3 Chromatin?Regulating Enzymes

11.1.4 Measuring DNA Methylation Levels and Histone Marks Experimentally

11.2 Working with Epigenetic Data

11.2.1 Processing of DNA Methylation Data

11.2.1.1 Imputation of Missing Values

11.2.1.2 Smoothing of DNA Methylation Data

11.2.2 Differential Methylation Analysis

11.2.3 Comethylation Analysis

11.2.4 Working with Data on Histone Marks

11.3 Chromatin States

11.3.1 Measuring Chromatin States

11.3.2 Connecting Epigenetic Marks and Gene Expression by Linear Models

11.3.3 Markov Models and Hidden Markov Models

11.3.4 Architecture of a Hidden Markov Model

11.3.5 Elements of an HMM

11.4 The Role of Epigenetics in Cellular Differentiation and Reprogramming

11.4.1 Short History of Stem Cell Research

11.4.2 Developmental Gene Regulatory Networks

11.5 The Role of Epigenetics in Cancer and Complex Diseases

11.6 Summary

11.7 Problems

Bibliography

Chapter 12 Metabolic Networks

12.1 Introduction

12.2 Resources on Metabolic Network Representations

12.3 Stoichiometric Matrix

12.4 Linear Algebra Primer

12.4.1 Matrices: Definitions and Notations

12.4.2 Adding, Subtracting, and Multiplying Matrices

12.4.3 Linear Transformations, Ranks, and Transpose

12.4.4 Square Matrices and Matrix Inversion

12.4.5 Eigenvalues of Matrices

12.4.6 Systems of Linear Equations

12.5 Flux Balance Analysis

12.5.1 Gene Knockouts: MOMA Algorithm

12.5.2 OptKnock Algorithm

12.6 Double Description Method

12.7 Extreme Pathways and Elementary Modes

12.7.1 Steps of the Extreme Pathway Algorithm

12.7.2 Analysis of Extreme Pathways

12.7.3 Elementary Flux Modes

12.7.4 Pruning Metabolic Networks: NetworkReducer

12.8 Minimal Cut Sets

12.8.1 Applications of Minimal Cut Sets

12.9 High?Flux Backbone

12.10 Summary

12.11 Problems

12.11.1 Static Network Properties: Pathways

Bibliography

Chapter 13 Kinetic Modeling of Cellular Processes

13.1 Biological Oscillators

13.2 Circadian Clocks

13.2.1 Role of Post?transcriptional Modifications

13.3 Ordinary Differential Equation Models

13.3.1 Examples for ODEs

13.4 Modeling Cellular Feedback Loops by ODEs

13.4.1 Protein Synthesis and Degradation: Linear Response

13.4.2 Phosphorylation/Dephosphorylation – Hyperbolic Response

13.4.3 Phosphorylation/Dephosphorylation – Buzzer

13.4.4 Perfect Adaptation – Sniffer

13.4.5 Positive Feedback – One?Way Switch

13.4.6 Mutual Inhibition – Toggle Switch

13.4.7 Negative Feedback – Homeostasis

13.4.8 Negative Feedback: Oscillatory Response

13.4.9 Cell Cycle Control System

13.5 Partial Differential Equations

13.5.1 Spatial Gradients of Signaling Activities

13.5.2 Reaction–Diffusion Systems

13.6 Dynamic Phosphorylation of Proteins

13.7 Summary

13.8 Problems

Bibliography

Chapter 14 Stochastic Processes in Biological Cells

14.1 Stochastic Processes

14.1.1 Binomial Distribution

14.1.2 Poisson Process

14.1.3 Master Equation

14.2 Dynamic Monte Carlo (Gillespie Algorithm)

14.2.1 Basic Outline of the Gillespie Method

14.3 Stochastic Effects in Gene Transcription

14.3.1 Expression of a Single Gene

14.3.2 Toggle Switch

14.4 Stochastic Modeling of a Small Molecular Network

14.4.1 Model System: Bacterial Photosynthesis

14.4.2 Pools?and?Proteins Model

14.4.3 Evaluating the Binding and Unbinding Kinetics

14.4.4 Pools of the Chromatophore Vesicle

14.4.5 Steady?State Regimes of the Vesicle

14.5 Parameter Optimization with Genetic Algorithm

14.6 Protein–Protein Association

14.7 Brownian Dynamics Simulations

14.8 Summary

14.9 Problems

14.9.1 Dynamic Simulations of Networks

Bibliography

Chapter 15 Integrated Cellular Networks

15.1 Response of Gene Regulatory Network to Outside Stimuli

15.2 Whole?Cell Model of Mycoplasma genitalium

15.3 Architecture of the Nuclear Pore Complex

15.4 Integrative Differential Gene Regulatory Network for Breast Cancer Identified Putative Cancer Driver Genes

15.5 Particle Simulations

15.6 Summary

Bibliography

Chapter 16 Outlook

Index