BIOMOLECULAR NETWORKS -- CONTENTS -- PREFACE -- ACKNOWLEDGMENTS -- LIST OF ILLUSTRATIONS -- ACRONYMS -- 1 Introduction -- 1.1 Basic Concepts in Molecular Biology -- 1.1.1 Genomes, Genes, and DNA Replication Process -- 1.1.2 Transcription Process for RNA Synthesis -- 1.1.3 Translation Process for Protein Synthesis -- 1.2 Biomolecular Networks in Cells -- 1.3 Network Systems Biology -- 1.4 About This Book -- I GENE NETWORKS -- 2 Transcription Regulation: Networks and Models -- 2.1 Transcription Regulation and Gene Expression -- 2.1.1 Transcription and Gene Regulation -- 2.1.2 Microarray Experiments and Databases -- 2.1.3 ChIP-Chip Technology and Transcription Factor Databases -- 2.2 Networks in Transcription Regulation -- 2.3 Nonlinear Models Based on Biochemical Reactions -- 2.4 Integrated Models for Regulatory Networks -- 2.5 Summary -- 3 Reconstruction of Gene Regulatory Networks -- 3.1 Mathematical Models of Gene Regulatory Network -- 3.1.1 Boolean Networks -- 3.1.2 Bayesian Networks -- 3.1.3 Markov Networks -- 3.1.4 Differential Equations -- 3.2 Reconstructing Gene Regulatory Networks -- 3.2.1 Singular Value Decomposition -- 3.2.2 Model-Based Optimization -- 3.3 Inferring Gene Networks from Multiple Datasets -- 3.3.1 General Solutions and a Particular Solution of Network Structures for Multiple Datasets -- 3.3.2 Decomposition Algorithm -- 3.3.3 Numerical Validation -- 3.4 Gene Network-Based Drug Target Identification -- 3.4.1 Network Identification Methods -- 3.4.2 Linear Programming Framework -- 3.5 Summary -- 4 Inference of Transcriptional Regulatory Networks -- 4.1 Predicting TF Binding Sites and Promoters -- 4.2 Inference of Transcriptional Interactions -- 4.2.1 Differential Equation Methods -- 4.2.2 Bayesian Approaches -- 4.2.3 Data Mining and Other Methods -- 4.3 Identifying Combinatorial Regulations of TFs.

4.4 Inferring Cooperative Regulatory Networks -- 4.4.1 Mathematical Models -- 4.4.2 Estimating TF Activity -- 4.4.3 Linear Programming Models -- 4.4.4 Numerical Validation -- 4.5 Prediction of Transcription Factor Activity -- 4.5.1 Matrix Factorization -- 4.5.2 Nonlinear Models -- 4.6 Summary -- II PROTEIN INTERACTION NETWORKS -- 5 Prediction of Protein-Protein Interactions -- 5.1 Experimental Protein-Protein Interactions -- 5.2 Prediction of Protein-Protein Interactions -- 5.2.1 Association Methods -- 5.2.2 Maximum-Likelihood Estimation -- 5.2.3 Deterministic Optimization Approaches -- 5.3 Protein Interaction Prediction Based on Multidomain Pairs -- 5.3.1 Cooperative Domains, Strongly Cooperative Domains, Superdomains -- 5.3.2 Inference of Multidomain Interactions -- 5.3.3 Numerical Validation -- 5.3.4 Reconstructing Complexes by Multidomain Interactions -- 5.4 Domain Interaction Prediction Methods -- 5.4.1 Statistical Method -- 5.4.2 Domain Pair Exclusion Analysis -- 5.4.3 Parsimony Explanation Approaches -- 5.4.4 Integrative Approaches -- 5.5 Summary -- 6 Topological Structure of Biomolecular Networks -- 6.1 Statistical Properties of Biomolecular Networks -- 6.2 Evolution of Protein Interaction Networks -- 6.3 Hubs, Motifs, and Modularity in Biomolecular Networks -- 6.3.1 Network Centralities and Hubs -- 6.3.2 Network Modularity and Motifs -- 6.4 Explorative Roles of Hubs and Network Motifs -- 6.4.1 Dynamic Modularity Organized by Hubs and Network Motifs -- 6.4.2 Network Motifs Acting as Connectors between Pathways -- 6.5 Modularity Evaluation of Biomolecular Networks -- 6.5.1 Modularity Density D -- 6.5.2 Improving Module Resolution Limits by D -- 6.5.3 Equivalence between D and Kernel k Means -- 6.5.4 Extension of D to General Criteria: D(λ) and D(w) -- 6.5.5 Numerical Validation -- 6.6 Summary -- 7 Alignment of Biomolecular Networks.

7.1 Biomolecular Networks from Multiple Species -- 7.2 Pairwise Alignment of Biomolecular Networks -- 7.2.1 Score-Based Algorithms -- 7.2.2 Evolution-Guided Method -- 7.2.3 Graph Matching Algorithm -- 7.3 Network Alignment by Mathematical Programming -- 7.3.1 Integer Programming Formulation -- 7.3.2 Components of the Integer Quadratic Programming Approach -- 7.3.3 Numerical Validation -- 7.4 Multiple Alignment of Biomolecular Networks -- 7.5 Subnetwork and Pathway Querying -- 7.6 Summary -- 8 Network-Based Prediction of Protein Function -- 8.1 Protein Function and Annotation -- 8.2 Protein Functional Module Detection -- 8.2.1 Distance-Based Clustering Methods -- 8.2.2 Graph Clustering Methods -- 8.2.3 Validation of Module Detection -- 8.3 Functional Linkage for Protein Function Annotation -- 8.3.1 Bayesian Approach -- 8.3.2 Hopfield Network Method -- 8.3.3 p-Value Method -- 8.3.4 Statistical Framework -- 8.4 Protein Function Prediction from High-Throughput Data -- 8.4.1 Neighborhood Approaches -- 8.4.2 Optimization Methods -- 8.4.3 Probabilistic Methods -- 8.4.4 Machine Learning Techniques -- 8.5 Function Annotation Methods for Domains -- 8.5.1 Domain Sources -- 8.5.2 Integration of Heterogeneous Data -- 8.5.3 Domain Function Prediction -- 8.5.4 Numerical Validation -- 8.6 Summary -- III METABOLIC NETWORKS AND SIGNALING NETWORKS -- 9 Metabolic Networks: Analysis, Reconstruction, and Application -- 9.1 Cellular Metabolism and Metabolic Pathways -- 9.2 Metabolic Network Analysis and Modeling -- 9.2.1 Flux Balance Analysis -- 9.2.2 Elementary Mode and Extreme Pathway Analysis -- 9.2.3 Modeling Metabolic Networks -- 9.3 Reconstruction of Metabolic Networks -- 9.3.1 Pathfinding Based on Reactions and Compounds -- 9.3.2 Stoichiometric Approaches Based on Flux Profiles -- 9.3.3 Inferring Biochemical Networks from Timecourse Data.

9.4 Drug Target Detection in Metabolic Networks -- 9.4.1 Drug Target Detection Problem -- 9.4.2 Integer Linear Programming Model -- 9.4.3 Numerical Validation -- 9.5 Summary -- 10 Signaling Networks: Modeling and Inference -- 10.1 Signal Transduction in Cellular Systems -- 10.2 Modeling of Signal Transduction Pathways -- 10.2.1 Differential Equation Models -- 10.2.2 Petri Net Models -- 10.3 Inferring Signaling Networks from High-Throughput Data -- 10.3.1 NetSearch Method -- 10.3.2 Ordering Signaling Components -- 10.3.3 Color-Coding Methods -- 10.4 Inferring Signaling Networks by Linear Programming -- 10.4.1 Integer Linear Programming Model -- 10.4.2 Significance Measures -- 10.4.3 Numerical Validation -- 10.4.4 Inferring Signaling Networks by Network Flow Models -- 10.5 Inferring Signaling Networks from Experimental Evidence -- 10.6 Summary -- 11 Other Topics and New Trends -- 11.1 Network-Based Protein Structural Analysis -- 11.2 Integration of Biomolecular Networks -- 11.3 Posttranscriptional Regulation of Noncoding RNAs -- 11.4 Biomolecular Interactions and Human Diseases -- 11.5 Summary -- REFERENCES -- INDEX.