Parallel Computing for Bioinformatics and Computational Biology
Models, Enabling Technologies, and Case Studies
Wiley Series on Parallel and Distributed Computing
1. Edition May 2006
816 Pages, Hardcover
Practical Approach Book
Short Description
Parallel Computing for Bioinformatics is the first book to deal with the topic of parallel computing and bioinformatics. Written by renowned experts and well-reputed researchers in this emerging field, it provides an opportunity for researchers to explore the rich and complex subject of ?Bioinformatics?. The book presents case studies that deal with a variety of difficult problems in Bioinformatics and how parallel computing is used to produce better results in a more efficient manner with faster rates of computation, therefore enabling more complex bioinformatics applications and larger and richer data sets. With a mixture of algorithmics, experiments, and simulations, this title offers not only qualitative but also quantitative insights into the rich field of bioinformatics.
Discover how to streamline complex bioinformatics applications with parallel computing
This 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 rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics.
Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication.
The work is organized into five parts:
* Algorithms and models
* Sequence analysis and microarrays
* Phylogenetics
* Protein folding
* Platforms and enabling technologies
Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries.
Contributors.
Acknowledgments.
PART I: ALGORITHMS AND MODELS.
1 Parallel and Evolutionary Approaches to Computational Biology (Nouhad J. Rizk).
References.
2 Parallel Monte Carlo Simulation of HIV Molecular Evolution in Response to Immune Surveillance (Jack da Silva).
References.
3 Differential Evolutionary Algorithms for In Vivo Dynamic Analysis of Glycolysis and Pentose Phosphate Pathway in Escherichia coli (Christophe Chassagnole).
References.
4 Compute-Intensive Simulations for Cellular Models (K. Burrage).
References.
5 Parallel Computation in Simulating Diffusion and Deformation in Human Brain (Ning KangI0.
References.
PART II: SEQUENCE ANALYSIS AND MICROARRAYS.
6 Computational Molecular Biology (Azzedine Boukerche).
References.
7 Special-Purpose Computing for Biological Sequence Analysis (Bertil Schmidt).
References.
8 Multiple Sequence Alignment in Parallel on a Cluster ofWorkstations (Amitava Datta).
References.
9 Searching Sequence Databases Using High-Performance BLASTs (Xue Wu).
References.
10 Parallel Implementations of Local Sequence Alignment: Hardware and Software (Vipin Chaudhary).
References.
11 Parallel Computing in the Analysis of Gene Expression Relationships (Robert L. Martino).
References.
12 Assembling DNA Fragments with a Distributed Genetic Algorithm (Gabriel Luque).
References.
13 A Cooperative Genetic Algorithm for Knowledge Discovery in Microarray Experiments (Mohammed Khabzaoui).
References.
PART III: PHYLOGENETICS.
14 Parallel and Distributed Computation of Large Phylogenetic Trees (Alexandros Stamatakis).
References.
15 Phylogenetic Parameter Estimation on COWs (Ekkehard Petzold).
References.
16 High-Performance Phylogeny Reconstruction Under Maximum Parsimony (Tiffani L. Williams).
References.
PART IV: PROTEIN FOLDING.
17 Protein Folding with the Parallel Replica Exchange Molecular Dynamics Method (Ruhong Zhou).
References.
18 High-Performance Alignment Methods for Protein Threading (R. Andonov).
References.
19 Parallel Evolutionary Computations in Discerning Protein Structures (Richard O. Day).
References.
PART V: PLATFORMS AND ENABLING TECHNOLOGIES.
20 A Brief Overview of Grid Activities for Bioinformatics and Health Applications (Ali Al Mazari).
References.
21 Parallel Algorithms for Bioinformatics (Shahid H. Bokhari).
References.
22 Cluster and Grid Infrastructure for Computational Chemistry and Biochemistry (Kim K. Baldridge).
References.
23 DistributedWorkflows in Bioinformatics (Arun Krishnan).
References.
24 Molecular Structure Determination on a Computational and Data Grid (Russ Miller).
References.
25 GIPSY: A Problem-Solving Environment for Bioinformatics Applications (Rajendra R. Joshi).
References.
26 TaskSpaces: A Software Framework for Parallel Bioinformatics on Computational Grids (Hans De Sterck).
References.
27 The Organic Grid: Self-Organizing Computational Biology on Desktop Grids (Arjav J. Chakravarti).
References.
28 FPGA Computing in Modern Bioinformatics (H. Simmler).
References.
29 Virtual Microscopy: Distributed Image Storage, Retrieval, Analysis, and Visualization (T. Pan).
References.
Index.
"...a building block on computational biology concepts to help researchers and students work on more innovative ideas." (IEEE Distributed Systems Online, March 2007)
"...a good overview of the current state of computing in these areas." (CHOICE, November 2006)
"...this book presents researchers in computational biology, bioinformatics, mathematics, statistics, and computer science with the opportunity to explore this interdisciplinary research area..." (Computing Reviews.com, September 27, 2006)
