LEADER 05675nam 2200733Ia 450 001 9910143084903321 005 20230105010207.0 010 $a1-282-18602-7 010 $a9786612186028 010 $a0-470-48806-9 010 $a0-470-48805-0 035 $a(CKB)1000000000773803 035 $a(EBL)448827 035 $a(SSID)ssj0000112215 035 $a(PQKBManifestationID)11128382 035 $a(PQKBTitleCode)TC0000112215 035 $a(PQKBWorkID)10087004 035 $a(PQKB)11715927 035 $a(Au-PeEL)EBL448827 035 $a(CaPaEBR)ebr10315655 035 $a(CaONFJC)MIL218602 035 $a(MiAaPQ)EBC448827 035 $a(OCoLC)441892308 035 $a(EXLCZ)991000000000773803 100 $a20090226d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBiomolecular networks$b[electronic resource] $emethods and applications in systems biology /$fLuonan Chen, Rui-Sheng Wang, Xiang-Sun Zhang 210 $aHoboken, NJ $cWiley$dc2009 215 $a1 online resource (420 p.) 225 1 $aWiley series on bioinformatics 300 $aDescription based upon print version of record. 311 $a0-470-24373-2 320 $aIncludes bibliographical references and index. 327 $aBIOMOLECULAR 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 327 $a2.1.3 ChIP-Chip Technology and Transcription Factor Databases2.2 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 327 $a3.3.1 General Solutions and a Particular Solution of Network Structures for Multiple Datasets3.3.2 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 327 $a4.4 Inferring Cooperative Regulatory Networks4.4.1 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 327 $a5.3 Protein Interaction Prediction Based on Multidomain Pairs5.3.1 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 327 $a6.2 Evolution of Protein Interaction Networks 330 $aAlternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the impor 410 0$aWiley series on bioinformatics. 606 $aMolecular biology$xData processing 606 $aComputational biology 606 $aBioinformatics 606 $aBiological systems$xResearch$xData processing 615 0$aMolecular biology$xData processing. 615 0$aComputational biology. 615 0$aBioinformatics. 615 0$aBiological systems$xResearch$xData processing. 676 $a572.80285 700 $aChen$b Luonan$f1962-$0879064 701 $aWang$b Rui-Sheng$0879065 701 $aZhang$b Xiang-Sun$f1943-$0879066 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143084903321 996 $aBiomolecular networks$91963075 997 $aUNINA