Parallel Computing for Bioinformatics and Computational Biology Models Enabling Technologies and Case Studies Wiley Series on Parallel and Distributed Computing 1st Edition by Albert Zomaya ISBN 978-0471756491 0471756491

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ISBN 10: 0471756491  

ISBN 13:  978-0471756491

Author: Albert Y. Zomaya
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.

Table of contents: 

PART I: ALGORITHMS AND MODELS

1. Parallel and Evolutionary Approaches to Computational Biology (Nouhad J. Rizk)
    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 (Jack da Silva)
    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 Directions
    References

3. Differential Evolutionary Algorithms for In Vivo Dynamic Analysis of Glycolysis and Pentose Phosphate Pathway in E. coli (Christophe Chassagnole)
    3.1 Introduction
    3.2 Mathematical Model
    3.3 Estimation of the Parameters
    3.4 Kinetic Parameter Estimation by DE
    3.5 Simulation and Results
    3.6 Stability Analysis
    3.7 Control Characteristics
    3.8 Conclusions
    References

4. Compute-Intensive Simulations for Cellular Models (K. Burrage)
    4.1 Introduction
    4.2 Simulation Methods for Stochastic Chemical Kinetics
    4.3 Genetic Regulation
    4.4 Parallel Computing for Biological Systems
    4.5 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 (Ning Kang)
    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 (Azzedine Boukerche)
    6.1 Introduction
    6.2 Basic Concepts in Molecular Biology
    6.3 Global and Local Biological Sequence Alignment
    6.4 Heuristic Approaches for Sequence Comparison
    6.5 Parallel and Distributed Sequence Comparison
    6.6 Conclusions
    References

7. Special-Purpose Computing for Biological Sequence Analysis (Bertil Schmidt)
    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 (Amitava Datta)
    8.1 Introduction
    8.2 CLUSTALW
    8.3 Implementation
    8.4 Results
    8.5 Conclusion
    References

9. Searching Sequence Databases Using High-Performance BLASTs (Xue Wu)
    9.1 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 (Vipin Chaudhary)
     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 (Robert L. Martino)
     11.1 Significance of Gene Expression Analysis
     11.2 Multivariate Gene Expression Relations
     11.3 Classification Based on Gene Expression
     11.4 Discussion and Future Directions
     References

12. Assembling DNA Fragments with a Distributed Genetic Algorithm (Gabriel Luque)
     12.1 Introduction
     12.2 DNA Fragment Assembly Problem
     12.3 Sequential GA
     12.4 Parallel GA
     12.5 Experimental Results
     12.6 Conclusions
     References

13. A Cooperative Genetic Algorithm for Knowledge Discovery in Microarray Experiments (Mohammed Khabzaoui)
     13.1 Introduction
     13.2 Microarray Experiments
     13.3 Association Rules
     13.4 Multi-Objective Genetic Algorithm
     13.5 Cooperative Multi-Objective Genetic Algorithm (PMGA)
     13.6 Experiments
     13.7 Conclusion
     References

PART III: PHYLOGENETICS

14. Parallel and Distributed Computation of Large Phylogenetic Trees (Alexandros Stamatakis)
     14.1 Introduction
     14.2 Maximum Likelihood
     14.3 State-of-the-Art ML Programs
     14.4 Algorithmic Solutions in RAxML-III
     14.5 HPC Solutions in RAxML-III
     14.6 Future Developments
     References

15. Phylogenetic Parameter Estimation on COWs (Ekkehard Petzold)
     15.1 Introduction
     15.2 Quartet Puzzling
     15.3 Hardware, Data, and Scheduling Algorithms
     15.4 Parallelizing PEst
     15.5 Extending Parallel Coverage
     15.6 Discussion
     References

16. High-Performance Phylogeny Reconstruction Under Maximum Parsimony (Tiffani L. Williams)
     16.1 Introduction
     16.2 Maximum Parsimony
     16.3 Parallel Branch and Bound
     16.4 Disk-Covering Methods
     16.5 Summary and Open Problems
     References

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PART IV: PROTEIN FOLDING

17. Protein Folding with the Parallel Replica Exchange Molecular Dynamics Method (Ruhong Zhou)
     17.1 Introduction
     17.2 REMD Method
     17.3 Protein Folding with REMD
     17.4 Protein Structure Refinement with REMD
     17.5 Summary
     References

18. High-Performance Alignment Methods for Protein Threading (R. Andonov)
     18.1 Introduction
     18.2 Formal Definition
     18.3 Mixed Integer Programming Models
     18.4 Divide-and-Conquer Technique
     18.5 Parallelization
     18.6 Future Research Directions
     18.7 Conclusion
     18.8 Summary
     References

19. Parallel Evolutionary Computations in Discerning Protein Structures (Richard O. Day)
     19.1 Introduction
     19.2 PSP Problem
     19.3 Protein Structure Discerning Methods
     19.4 PSP Energy Minimization EAs
     19.5 PSP Parallel EA Performance Evaluation
     19.6 Results and Discussion
     19.7 Conclusions and Suggested Research
     References

PART V: PLATFORMS AND ENABLING TECHNOLOGIES

20. A Brief Overview of Grid Activities for Bioinformatics and Health Applications (Ali Al Mazari)
     20.1 Introduction
     20.2 Grid Computing
     20.3 Bioinformatics and Health Applications
     20.4 Grid Computing for Bioinformatics
     20.5 Grid Activities in Europe, UK, USA, Asia, and Japan
     20.9 International Grid Collaborations
     20.11 Conclusions and Future Trends
     References

21. Parallel Algorithms for Bioinformatics (Shahid H. Bokhari)
     21.1 Introduction
     21.2 Parallel Computer Architecture
     21.3 Bioinformatics Algorithms on the Cray MTA System
     21.4 Summary
     References

22. Cluster and Grid Infrastructure for Computational Chemistry and Biochemistry (Kim K. Baldridge)
     22.1 Introduction
     22.2 GAMESS Execution on Clusters
     22.3 Portal Technology
     22.4 Running GAMESS with Nimrod Grid Infrastructure
     22.5 Computational Chemistry Workflow Environments
     22.6 Conclusions
     References

23. Distributed Workflows in Bioinformatics (Arun Krishnan)
     23.1 Introduction
     23.2 Challenges of Grid Computing
     23.3 Grid Applications and Programming
     23.4 Grid Execution Language
     23.5 GUI-Based Workflow Construction and Execution
     23.6 Case Studies
     23.7 Summary
     References

24. Molecular Structure Determination on a Computational and Data Grid (Russ Miller)
     24.1 Introduction
     24.2 Molecular Structure Determination
     24.3 Grid Computing in Buffalo
     24.4 Center for Computational Research
     24.5 ACDC-Grid Overview
     24.6 Research Collaborations and Advancements
     24.7 Application Abstractions and Tools
     24.8 Conclusions
     References

25. GIPSY: A Problem-Solving Environment for Bioinformatics Applications (Rajendra R. Joshi)
     25.1 Introduction
     25.2 Architecture
     25.3 Currently Deployed Applications
     25.4 Conclusion
     References

26. TaskSpaces: A Software Framework for Parallel Bioinformatics on Computational Grids (Hans De Sterck)
     26.1 Introduction
     26.2 The TaskSpaces Framework
     26.3 Application: RNA Motif Folding
     26.4 Case Study on Computational Grid
     26.5 Results and Future Work
     26.6 Summary and Conclusion
     References

27. The Organic Grid: Self-Organizing Computational Biology on Desktop Grids (Arjav J. Chakravarti)
     27.1 Introduction
     27.2 Background and Related Work
     27.3 Measurements
     27.4 Conclusions and Future Directions
     References

28. FPGA Computing in Modern Bioinformatics (H. Simmler)
     28.1 Parallel Processing Models
     28.2 Image Processing Task
     28.3 FPGA Hardware Accelerators
     28.4 Case Study: Protein Structure Prediction
     28.5 Conclusion
     References

29. Virtual Microscopy: Distributed Image Storage, Retrieval, Analysis, and Visualization (T. Pan)
     29.1 Introduction
     29.2 Architecture
     29.3 Image Analysis
     29.4 Clinical Use
     29.5 Education
     29.6 Future Directions
     29.7 Summary

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Tags: Albert Zomaya, Parallel Computing, for Bioinformatics, Computational Biology