Parallel computing for bioinformatics and computational biology [[electronic resource] ] : models, enabling technologies, and case studies / / edited by Albert Y. Zomaya

• Record Nr.: UNINA9910143576103321
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley-Interscience, c2006
• ISBN: 1-280-46270-1; 9786610462704; 0-470-36220-0; 0-471-75650-4; 0-471-75649-0
• Descrizione fisica: 1 online resource (814 p.)
• Collana: Wiley series on parallel and distributed computing
• Altri autori (Persone): ZomayaAlbert Y
• Disciplina: 570.285435; 572.8/0285
• Soggetti: Bioinformatics; Computational biology; Parallel processing (Electronic computers); Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: PARALLEL 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; 2.7 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; 4.4 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; 6.4 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; 9 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; 11 Parallel Computing in the Analysis of Gene Expression Relationships
• Sommario/riassunto: Discover how to streamline complex bioinformatics applications with parallel computingThis publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution.A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster