Protein Interaction Networks

Computational Analysis

By: Aidong Zhang

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Hardcover | 6 April 2009

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292 Pages

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The analysis of protein-protein interactions is fundamental to the understanding of cellular organisation, processes, and functions. Proteins seldom act as single isolated species; rather, proteins involved in the same cellular processes often interact with each other. Functions of uncharacterised proteins can be predicted through comparison with the interactions of similar known proteins.

Recent large-scale investigations of protein-protein interactions using such techniques as two-hybrid systems, mass spectrometry, and protein microarrays have enriched the available protein interaction data and facilitated the construction of integrated protein-protein interaction networks. The resulting large volume of protein-protein interaction data has posed a challenge to experimental investigation. This book provides a comprehensive understanding of the computational methods available for the analysis of protein-protein interaction networks. It offers an in-depth survey of a range of approaches, including statistical, topological, data-mining, and ontology-based methods.

The author discusses the fundamental principles underlying each of these approaches and their respective benefits and drawbacks, and she offers suggestions for future research.

Industry Reviews

"Up-to-date on the research and thoroughly comprehensive in coverage, Aidong Zhang's Protein Interaction Networks: Computational Analysis is an invaluable contribution to our understanding and knowledge of current analytic methods for protein interaction networks. Written with technical depth and sophistication, and replete with examples, this book will be both an indispensable manual for practitioners and a crucial textbook for teaching." Jiawei Han, Professor of Computer Science, University of Illinois at Urbana-Champaign "This book provides a comprehensive coverage of current research issues and solutions in protein interaction networks. Within this new and exciting area of research, certain topics are explored in depth, including newest results reported by the author and other leading experts. I highly recommend this book for researchers and students who are interested in bioinformatics." Yi Pan, Chair and Professor of Computer Science, Georgia State University "This book provides the most comprehensive and systematic review to an important biomedical research topic (protein interaction network). It gives its readers an opportunity to easily learn about this challenging topic and to begin investigating how they may contribute to it. Its great value makes it suitable for a broad range of readers, from students to experienced researchers." Dong Xu, Professor and Chair of the Computer Science Department, University of Missouri, Columbia "This book is a comprehensive and an excellent introduction to network biology in general and proteins networks in particular. It provides detailed description of the major computational concepts and their applications in systems biology. It is a must have book for anyone interested in this exciting topic." Mohammed J. Zaki, Professor of Computer Science, Rensselaer Polytechnic Institute

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Preface	p. xiii
Introduction	p. 1
Rapid Growth of Protein-Protein Interaction Data	p. 1
Computational Analysis of PPI Networks	p. 3
Topological Features of PPI Networks	p. 4
Modularity Analysis	p. 5
Prediction of Protein Functions in PPI Networks	p. 6
Integration of Domain Knowledge	p. 7
Significant Applications	p. 7
Organization of this Book	p. 9
Summary	p. 10
Experimental Approaches to Generation of PPI Data	p. 11
Introduction	p. 11
The Y2H System	p. 11
Mass Spectrometry (MS) Approaches	p. 13
Protein Microarrays	p. 15
Public PPI Data and Their Reliability	p. 15
Experimental PPI Data Sets	p. 15
Public PPI Databases	p. 16
Functional Analysis of PPI Data	p. 17
Summary	p. 20
Computational Methods for the Prediction of PPIs	p. 21
Introduction	p. 21
Genome-Scale Approaches	p. 21
Sequence-Based Approaches	p. 25
Structure-Based Approaches	p. 26
Learning-Based Approaches	p. 27
Network Topology-Based Approaches	p. 29
Summary	p. 32
Basic Properties and Measurements of Protein Interaction Networks	p. 33
Introduction	p. 33
Representation of PPI Networks	p. 33
Basic Concepts	p. 34
Basic Centralities	p. 35
Degree Centrality	p. 35
Distance-Based Centralities	p. 35
Current-Flow-Based Centrality	p. 37
Random-Walk-Based Centrality	p. 40
Feedback-Based Centrality	p. 41
Characteristics of PPI Networks	p. 44
Summary	p. 49
Modularity Analysis of Protein Interaction Networks	p. 50
Introduction	p. 50
Useful Metrics for Modular Networks	p. 51
Cliques	p. 51
Cores	p. 51
Degree-Based Index	p. 52
Distance (Shortest Paths)-Based Index	p. 53
Methods for Clustering Analysis of Protein Interaction Networks	p. 53
Traditional Clustering Methods	p. 54
Nontraditional Clustering Methods	p. 55
Validation of Modularity	p. 56
Clustering Coefficient	p. 56
Validation Based on Agreement with Annotated Protein Function Databases	p. 57
Validation Based on the Definition of Clustering	p. 59
Topological Validation	p. 60
Supervised Validation	p. 61
Statistical Validation	p. 61
Validation of Protein Function Prediction	p. 62
Summary	p. 63
Topological Analysis of Protein Interaction Networks	p. 63
Introduction	p. 63
Overview and Analysis of Essential Network Components	p. 64
Error and Attack Tolerance of Complex Networks	p. 64
Role of High-Degree Nodes in Biological Networks	p. 67
Betweenness, Connectivity, and Centrality	p. 69
Bridging Centrality Measurements	p. 73
Performance of Bridging Centrality with Synthetic and Real-World Networks	p. 75
Assessing Network Disruption, Structural Integrity, and Modularity	p. 77
Network Modularization Using the Bridge Cut Algorithm	p. 84
Use of Bridging Nodes in Drug Discovery	p. 87
Biological Correlates of Bridging Centrality	p. 88
Results from Drug Discovery-Relevant Human Networks	p. 92
Comparison to Alternative Approaches: Yeast Cell Cycle State Space Network	p. 94
Potential of Bridging Centrality as a Drug Discovery Tool	p. 95
PathRatio: A Novel Topological Method for Predicting Protein Functions	p. 97
Weighted PPI Network	p. 97
Protein Connectivity and Interaction Reliability	p. 98
PathStrength and PathRatio Measurements	p. 99
Analysis of the PathRatio Topological Measurement	p. 100
Experimental Results	p. 101
Summary	p. 108
Distance-Based Modularity Analysis	p. 109
Introduction	p. 109
Topological Distance Measurement Based on Coefficients	p. 109
Distance Measurement by Network Distance	p. 112
PathRadio Method	p. 112
Averaging the Distances	p. 113
Ensemble Method	p. 114
Similarity Metrics	p. 115
Base Algorithms	p. 116
Consensus Methods	p. 116
Results of the Ensemble Methods	p. 118
UVcluster	p. 118
Similarity Learning Method	p. 120
Measurement of Biological Distance	p. 124
Sequence Similarity-Based Measurements	p. 124
Structural Similarity-Based Measurements	p. 125
Gene Expression Similarity-Based Measurements	p. 127
Summary	p. 128
Graph-Theoretic Approaches to Modularity Analysis	p. 130
Introduction	p. 130
Finding Dense Subgraphs	p. 130
Enumeration of Complete Subgraphs	p. 130
Monte Carlo Optimization	p. 131
Molecular Complex Detection	p. 132
Clique Percolation	p. 133
Merging by Statistical Significance	p. 134
Super-Paramagnetic Clustering	p. 136
Finding the Best Partition	p. 137
Recursive Minimum Cut	p. 137
Restricted Neighborhood Search Clustering (RNSC)	p. 138
Betweenness Cut	p. 140
Markov Clustering	p. 140
Line Graph Generation	p. 143
Graph Reduction-Based Approach	p. 144
Graph Reduction	p. 144
Hierarchical Modularization	p. 146
Time Complexity	p. 147
k Effects on Graph Reduction	p. 147
Hierarchical Structure of Modules	p. 149
Summary	p. 150
Flow-Based Analysis of Protein Interaction Networks	p. 152
Introduction	p. 152
Protein Function Prediction Using the FunctionalFlow Algorithm	p. 153
CASCADE: A Dynamic Flow Simulation for Modularity Analysis	p. 155
Occurrence Probability and Related Models	p. 156
The CASCADE Algorithm	p. 158
Analysis of Prototypical Data	p. 160
Significance of Individual Clusters	p. 162
Analysis of Functional Annotation	p. 164
Comparative Assessment of CASCADE with Other Approaches	p. 169
Analysis of Robustness	p. 175
Analysis of Computational Complexity	p. 175
Advantages of the CASCADE Method	p. 176
Functional Flow Analysis in Weighted PPI Networks	p. 177
Functional Influence Model	p. 178
Functional Flow Simulation Algorithm	p. 179
Time Complexity of Flow Simulation	p. 180
Detection of Overlapping Modules	p. 181
Detection of Disjoint Modules	p. 189
Functional Flow Pattern Mining	p. 191
Summary	p. 198
Statistics and Machine Learning Based Analysis of Protein Interaction Networks	p. 199
Introduction	p. 199
Applications of Markov Random Field and Belief Propagation for Protein Function Prediction	p. 200
Protein Function Prediction Using Kernel-Based Statistical Learning Methods	p. 207
Protein Function Prediction Using Bayesian Networks	p. 211
Improving Protein Function Prediction Using Bayesian Integrative Methods	p. 213
Summary	p. 214
Integration of GO into the Analysis of Protein Interaction Networks	p. 216
Introduction	p. 216
GO structure	p. 217
GO Annotations	p. 217
Semantic Similarity-Based Integration	p. 218
Structure-Based Methods	p. 219
Information Content-Based Methods	p. 220
Combination of Structure and Information Content	p. 221
Semantic Interactivity-Based Integration	p. 223
Estimate of Interaction Reliability	p. 223
Functional Co-Occurrence	p. 224
Topological Significance	p. 225
Protein Lethality	p. 226
Functional Module Detection	p. 227
Statistical Assessment	p. 227
Supervised Validation	p. 229
Probabilistic Approaches for Function Prediction	p. 231
GO Index-Based Probabilistic Method	p. 231
Semantic Similarity-Based Probabilistic Method	p. 235
Summary	p. 241
Data Fusion in the Analysis of Protein Interaction Networks	p. 243
Introduction	p. 243
Integration of Gene Expression with PPI Networks	p. 243
Integration of Protein Domain Information with PPI Networks	p. 244
Integration of Protein Localization Information with PPI Networks	p. 245
Integration of Several Data Sources with PPI Networks	p. 247
Kernel-Based Methods	p. 247
Bayesian Model-Based Method	p. 249
Summary	p. 249
Conclusion	p. 251
Bibliography	p. 255
Index	p. 273
Table of Contents provided by Ingram. All Rights Reserved.

Protein Interaction Networks

Computational Analysis

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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