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Biomolecular Networks: Methods and Applications in Systems Biology [Hardback]

Series edited by (University of Western Australia), Edited by (Osaka Sangyo University, Osaka, Japan), Series edited by (Department of Computer Science, Georgia State University), Edited by (Renmin University of China), Edited by (Chinese Academy of Sciences)
  • Formāts: Hardback, 416 pages, height x width x depth: 242x160x25 mm, weight: 694 g
  • Sērija : Wiley Series in Bioinformatics
  • Izdošanas datums: 28-Jul-2009
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 0470243732
  • ISBN-13: 9780470243732
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  • Formāts: Hardback, 416 pages, height x width x depth: 242x160x25 mm, weight: 694 g
  • Sērija : Wiley Series in Bioinformatics
  • Izdošanas datums: 28-Jul-2009
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 0470243732
  • ISBN-13: 9780470243732
Citas grāmatas par šo tēmu:
Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach.

Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods:





GENE NETWORKSTranscriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks



PROTEIN INTERACTION NETWORKSPrediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function



METABOLIC NETWORKS AND SIGNALING NETWORKSAnalysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends





In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.
Preface xiii
Acknowledgments xv
List Of Illustrations
xvii
Acronyms xxiii
Introduction
1(22)
Basic Concepts in Molecular Biology
1(7)
Genomes, Genes, and DNA Replication Process
5(1)
Transcription Process for RNA Synthesis
6(1)
Translation Process for Protein Synthesis
7(1)
Biomolecular Networks in Cells
8(5)
Network Systems Biology
13(5)
About This Book
18(5)
I GENE NETWORKS
23(96)
Transcription Regulation: Networks and Models
25(22)
Transcription Regulation and Gene Expression
25(7)
Transcription and Gene Regulation
25(3)
Microarray Experiments and Databases
28(2)
ChIP-Chip Technology and Transcription Factor Databases
30(2)
Networks in Transcription Regulation
32(4)
Nonlinear Models Based on Biochemical Reactions
36(7)
Integrated Models for Regulatory Networks
43(1)
Summary
44(3)
Reconstruction of Gene Regulatory Networks
47(42)
Mathematical Models of Gene Regulatory Network
47(8)
Boolean Networks
48(1)
Bayesian Networks
49(3)
Markov Networks
52(1)
Differential Equations
53(2)
Reconstructing Gene Regulatory Networks
55(6)
Singular Value Decomposition
56(2)
Model-Based Optimization
58(3)
Inferring Gene Networks from Multiple Datasets
61(11)
General Solutions and a Particular Solution of Network Structures for Multiple Datasets
63(2)
Decomposition Algorithm
65(2)
Numerical Validation
67(5)
Gene Network-Based Drug Target Identification
72(15)
Network Identification Methods
73(4)
Linear Programming Framework
77(10)
Summary
87(2)
Inference of Transcriptional Regulatory Networks
89(30)
Predicting TF Binding Sites and Promoters
89(3)
Inference of Transcriptional Interactions
92(7)
Differential Equation Methods
93(3)
Bayesian Approaches
96(2)
Data Mining and Other Methods
98(1)
Identifying Combinatorial Regulations of TFs
99(6)
Inferring Cooperative Regulatory Networks
105(9)
Mathematical Models
105(1)
Estimating TF Activity
106(2)
Linear Programming Models
108(1)
Numerical Validation
109(5)
Prediction of Transcription Factor Activity
114(4)
Matrix Factorization
114(3)
Nonlinear Models
117(1)
Summary
118(1)
II PROTEIN INTERACTION NETWORKS
119(160)
Prediction of Protein-Protein Interactions
121(48)
Experimental Protein-Protein Interactions
121(5)
Prediction of Protein-Protein Interactions
126(24)
Association Methods
127(7)
Maximum-Likelihood Estimation
134(5)
Deterministic Optimization Approaches
139(11)
Protein Interaction Prediction Based on Multidomain Pairs
150(13)
Cooperative Domains, Strongly Cooperative Domains, Superdomains
152(2)
Inference of Multidomain Interactions
154(3)
Numerical Validation
157(3)
Reconstructing Complexes by Multidomain Interactions
160(3)
Domain Interaction Prediction Methods
163(4)
Statistical Method
163(1)
Domain Pair Exclusion Analysis
163(1)
Parsimony Explanation Approaches
164(1)
Integrative Approaches
165(2)
Summary
167(2)
Topological Structure of Biomolecular Networks
169(36)
Statistical Properties of Biomolecular Networks
169(4)
Evolution of Protein Interaction Networks
173(1)
Hubs, Motifs, and Modularity in Biomolecular Networks
174(5)
Network Centralities and Hubs
174(3)
Network Modularity and Motifs
177(2)
Explorative Roles of Hubs and Network Motifs
179(15)
Dynamic Modularity Organized by Hubs and Network Motifs
180(6)
Network Motifs Acting as Connectors between Pathways
186(8)
Modularity Evaluation of Biomolecular Networks
194(10)
Modularity Density D
195(1)
Improving Module Resolution Limits by D
196(2)
Equivalence Between D and Kernel k Means
198(1)
Extension of D to General Criteria: Dλ and Dw
199(1)
Numerical Validation
200(4)
Summary
204(1)
Alignment of Biomolecular Networks
205(26)
Biomolecular Networks from Multiple Species
205(2)
Pairwise Alignment of Biomolecular Networks
207(6)
Score-Based Algorithms
208(3)
Evolution-Guided Method
211(1)
Graph Matching Algorithm
212(1)
Network Alignment by Mathematical Programming
213(10)
Integer Programming Formulation
214(2)
Components of the Integer Quadratic Programming Approach
216(1)
Numerical Validation
217(6)
Multiple Alignment of Biomolecular Networks
223(2)
Subnetwork and Pathway Querying
225(3)
Summary
228(3)
Network-Based Prediction of Protein Function
231(48)
Protein Function and Annotation
231(3)
Protein Functional Module Detection
234(5)
Distance-Based Clustering Methods
235(1)
Graph Clustering Methods
236(2)
Validation of Module Detection
238(1)
Functional Linkage for Protein Function Annotation
239(10)
Bayesian Approach
239(2)
Hopfield Network Method
241(1)
p-Value Method
242(1)
Statistical Framework
243(6)
Protein Function Prediction from High-Throughput Data
249(16)
Neighborhood Approaches
250(1)
Optimization Methods
251(3)
Probabilistic Methods
254(2)
Machine Learning Techniques
256(9)
Function Annotation Methods for Domains
265(12)
Domain Sources
267(1)
Integration of Heterogeneous Data
268(2)
Domain Function Prediction
270(1)
Numerical Validation
271(6)
Summary
277(2)
III METABOLIC NETWORKS AND SIGNALING NETWORKS
279(74)
Metabolic Networks: Analysis, Reconstruction, and Application
281(32)
Cellular Metabolism and Metabolic Pathways
281(5)
Metabolic Network Analysis and Modeling
286(8)
Flux Balance Analysis
286(2)
Elementary Mode and Extreme Pathway Analysis
288(4)
Modeling Metabolic Networks
292(2)
Reconstruction of Metabolic Networks
294(6)
Pathfinding Based on Reactions and Compounds
294(3)
Stoichiometric Approaches Based on Flux Profiles
297(1)
Inferring Biochemical Networks from Timecourse Data
298(2)
Drug Target Detection in Metabolic Networks
300(11)
Drug Target Detection Problem
301(1)
Integer Linear Programming Model
302(3)
Numerical Validation
305(6)
Summary
311(2)
Signaling Networks: Modeling and Inference
313(32)
Signal Transduction in Cellular Systems
313(3)
Modeling of Signal Transduction Pathways
316(5)
Differential Equation Models
317(2)
Petri Net Models
319(2)
Inferring Signaling Networks from High-Throughput Data
321(5)
NetSearch Method
322(1)
Ordering Signaling Components
323(1)
Color-Coding Methods
324(2)
Inferring Signaling Networks by Linear Programming
326(15)
Integer Linear Programming Model
327(2)
Significance Measures
329(1)
Numerical Validation
329(9)
Inferring Signaling Networks by Network Flow Models
338(3)
Inferring Signaling Networks from Experimental Evidence
341(2)
Summary
343(2)
Other Topics and New Trends
345(8)
Network-Based Protein Structural Analysis
345(2)
Integration of Biomolecular Networks
347(2)
Posttranscriptional Regulation of Noncoding RNAs
349(1)
Biomolecular Interactions and Human Diseases
350(2)
Summary
352(1)
References 353(28)
Index 381
LUONAN CHEN, PhD, is a full professor in the Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka, Japan, and he is also the founding director of Institute of Systems Biology, Shanghai University, Shanghai, China. Dr. Chen's fields of interest include systems biology, bioinformatics, and nonlinear dynamics. RUI-SHENG WANG, PhD, is an assistant professor in the School of Information, Renmin University of China. Dr. Wang's research interests include bioinformatics, computational systems biology, and complex networks.

XIANG-SUN ZHANG is a full research professor in the Institute of Applied Math-ematics, Chinese Academy of Sciences. Professor Zhang's research interests include bioinformatics, systems biology, optimization theory, and related computational mathematics.