LEADER 05589nam 22006734a 450 001 9910830390803321 005 20230828212541.0 010 $a1-280-46270-1 010 $a9786610462704 010 $a0-470-36220-0 010 $a0-471-75650-4 010 $a0-471-75649-0 035 $a(CKB)1000000000355188 035 $a(EBL)261003 035 $a(OCoLC)77371248 035 $a(SSID)ssj0000217942 035 $a(PQKBManifestationID)11186871 035 $a(PQKBTitleCode)TC0000217942 035 $a(PQKBWorkID)10212464 035 $a(PQKB)11051224 035 $a(MiAaPQ)EBC261003 035 $a(EXLCZ)991000000000355188 100 $a20050413d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aParallel computing for bioinformatics and computational biology$b[electronic resource] $emodels, enabling technologies, and case studies /$fedited by Albert Y. Zomaya 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (814 p.) 225 1 $aWiley series on parallel and distributed computing 300 $aDescription based upon print version of record. 311 $a0-471-71848-3 320 $aIncludes bibliographical references and index. 327 $aPARALLEL COMPUTING FOR BIOINFORMATICS AND COMPUTATIONAL BIOLOGY; CONTENTS; Preface; Contributors; Acknowledgments; PART I ALGORITHMS AND MODELS; 1 Parallel and Evolutionary Approaches to Computational Biology; 1.1 Introduction; 1.2 Bioinformatics; 1.3 Evolutionary Computation Applied to Computational Biology; 1.4 Conclusions; References; 2 Parallel Monte Carlo Simulation of HIV Molecular Evolution in Response to Immune Surveillance; 2.1 Introduction; 2.2 The Problem; 2.3 The Model; 2.4 Parallelization with MPI; 2.5 Parallel Random Number Generation; 2.6 Preliminary Simulation Results 327 $a2.7 Future DirectionsReferences; 3 Differential Evolutionary Algorithms for In Vivo Dynamic Analysis of Glycolysis and Pentose Phosphate Pathway in Escherichia coli; 3.1 Introduction; 3.2 Mathematical Model; 3.3 Estimation of the Parameters of the Model; 3.4 Kinetic Parameter Estimation by DE; 3.5 Simulation and Results; 3.6 Stability Analysis; 3.7 Control Characteristic; 3.8 Conclusions; References; 4 Compute-Intensive Simulations for Cellular Models; 4.1 Introduction; 4.2 Simulation Methods for Stochastic Chemical Kinetics; 4.3 Aspects of Biology - Genetic Regulation 327 $a4.4 Parallel Computing for Biological Systems4.5 Parallel Simulations; 4.6 Spatial Modeling of Cellular Systems; 4.7 Modeling Colonies of Cells; References; 5 Parallel Computation in Simulating Diffusion and Deformation in Human Brain; 5.1 Introduction; 5.2 Anisotropic Diffusion Simulation in White Matter Tractography; 5.3 Brain Deformation Simulation in Image-Guided Neurosurgery; 5.4 Summary; References; PART II SEQUENCE ANALYSIS AND MICROARRAYS; 6 Computational Molecular Biology; 6.1 Introduction; 6.2 Basic Concepts in Molecular Biology; 6.3 Global and Local Biological Sequence Alignment 327 $a6.4 Heuristic Approaches for Biological Sequence Comparison6.5 Parallel and Distributed Sequence Comparison; 6.6 Conclusions; References; 7 Special-Purpose Computing for Biological Sequence Analysis; 7.1 Introduction; 7.2 Hybrid Parallel Computer; 7.3 Dynamic Programming Communication Pattern; 7.4 Performance Evaluation; 7.5 Future Work and Open Problems; 7.6 Tutorial; References; 8 Multiple Sequence Alignment in Parallel on a Cluster of Workstations; 8.1 Introduction; 8.2 CLUSTAL W; 8.3 Implementation; 8.4 Results; 8.5 Conclusion; References 327 $a9 Searching Sequence Databases Using High-Performance BLASTs9.1 Introduction; 9.2 Basic Blast Algorithm; 9.3 Blast Usage and Performance Factors; 9.4 High Performance BLASTs; 9.5 Comparing BLAST Performance; 9.6 UMD-BLAST; 9.7 Future Directions; 9.8 Related Work; 9.9 Summary; References; 10 Parallel Implementations of Local Sequence Alignment: Hardware and Software; 10.1 Introduction; 10.2 Sequence Alignment Primer; 10.3 Smith-Waterman Algorithm; 10.4 FASTA; 10.5 BLAST; 10.6 HMMER - Hidden Markov Models; 10.7 ClustalW; 10.8 Specialized Hardware: FPGA; 10.9 Conclusion; References 327 $a11 Parallel Computing in the Analysis of Gene Expression Relationships 330 $aDiscover how to streamline complex bioinformatics applications with parallel computingThis publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. 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