Parallel computing for bioinformatics and computational biology : models, enabling technologies, and case studies

Select an Action

Place Hold(s)
Add to My Lists
Email
Print

Title:

Publication Information:

Hoboken , NJ : John Wiley & Sons, 2006

ISBN:

9780471718482

Subject Term:

Bioinformatics

Computational biology

Parallel processing (Electronic computers)

Added Author:

Zomaya, Albert Y.

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010113134	QH324.2 P37 2006	Open Access Book	Book	Searching... Unknown

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.

Author Notes

ALBERT Y. ZOMAYA is the CISCO Systems Chair Professor of Internetworking, School of Information Technologies, The University of Sydney, and Deputy Director for Information Technology of the Sydney University Biological Informatics and Technology Centre. Professor Zomaya has been the Chair of the IEEE Technical Committee on Parallel Processing and has been awarded the IEEE Computer Society's Meritorious Service Award. He is an IEEE fellow.

Reviews 1

Choice Review

Data is spewing forth from biological labs at an incredible yet accelerating rate. A lab book and a pen might once have been sufficient for experiments that took months or years for an outcome, but new methods are needed for this Niagara of data. Bioinformatics is the attempt to use techniques from computer and information science to manage and analyze this large amount of complex data. Computational biology uses mathematical modeling and computational simulation techniques to study biological systems. Whether or not there is a worthwhile distinction between these areas, clearly they both need to use the latest and best in computer technology. This book discusses the uses of parallel computing in these areas from a variety of viewpoints. Some chapters focus on the biology and mathematical models, others look at algorithms and programming languages, and some look at the variety of parallel architectures. As with any multi-authored volume, there is unevenness across chapters, but this book offers a good overview of the current state of computing in these areas. ^BSumming Up: Recommended. Graduate students through professionals. P. Cull Oregon State University

Nouhad J. RizkJack da SilvaChristophe ChassagnoleK. BurrageNing KangAzzedine BoukercheBertil SchmidtAmitava DattaXue WuVipin ChaudharyRobert L. MartinoGabriel LuqueMohammed KhabzaouiAlexandros StamatakisEkkehard PetzoldTiffani L. WilliamsRuhong ZhouR. AndonovRichard O. DayAli Al MazariShahid H. BokhariKim K. BaldridgeArun KrishnanRuss MillerRajendra R. JoshiHans De SterckArjav J. ChakravartiH. SimmlerT. Pan

Preface	p. xv
Contributors	p. xxv
Acknowledgments	p. xxix
Part I Algorithms and Models	p. 1
1 Parallel and Evolutionary Approaches to Computational Biology	p. 3
1.1 Introduction	p. 4
1.2 Bioinformatics	p. 13
1.3 Evolutionary Computation Applied to Computational Biology	p. 20
1.4 Conclusions	p. 23
References	p. 25
2 Parallel Monte Carlo Simulation of HIV Molecular Evolution in Response to Immune Surveillance	p. 29
2.1 Introduction	p. 30
2.2 The Problem	p. 30
2.3 The Model	p. 32
2.4 Parallelization with MPI	p. 39
2.5 Parallel Random Number Generation	p. 43
2.6 Preliminary Simulation Results	p. 46
2.7 Future Directions	p. 52
References	p. 55
3 Differential Evolutionary Algorithms for In Vivo Dynamic Analysis of Glycolysis and Pentose Phosphate Pathway in Escherichia coli	p. 59
3.1 Introduction	p. 59
3.2 Mathematical Model	p. 61
3.3 Estimation of the Parameters of the Model	p. 67
3.4 Kinetic Parameter Estimation by DE	p. 69
3.5 Simulation and Results	p. 70
3.6 Stability Analysis	p. 73
3.7 Control Characteristic	p. 73
3.8 Conclusions	p. 75
References	p. 76
4 Compute-Intensive Simulations for Cellular Models	p. 79
4.1 Introduction	p. 79
4.2 Simulation Methods for Stochastic Chemical Kinetics	p. 81
4.3 Aspects of Biology - Genetic Regulation	p. 92
4.4 Parallel Computing for Biological Systems	p. 96
4.5 Parallel Simulations	p. 100
4.6 Spatial Modeling of Cellular Systems	p. 104
4.7 Modeling Colonies of Cells	p. 109
References	p. 115
5 Parallel Computation in Simulating Diffusion and Deformation in Human Brain	p. 121
5.1 Introduction	p. 121
5.2 Anisotropic Diffusion Simulation in White Matter Tractography	p. 122
5.3 Brain Deformation Simulation in Image-Guided Neurosurgery	p. 132
5.4 Summary	p. 142
References	p. 143
Part II Sequence Analysis and Microarrays	p. 147
6 Computational Molecular Biology	p. 149
6.1 Introduction	p. 149
6.2 Basic Concepts in Molecular Biology	p. 150
6.3 Global and Local Biological Sequence Alignment	p. 152
6.4 Heuristic Approaches for Biological Sequence Comparison	p. 158
6.5 Parallel and Distributed Sequence Comparison	p. 161
6.6 Conclusions	p. 164
References	p. 165
7 Special-Purpose Computing for Biological Sequence Analysis	p. 167
7.1 Introduction	p. 167
7.2 Hybrid Parallel Computer	p. 169
7.3 Dynamic Programming Communication Pattern	p. 172
7.4 Performance Evaluation	p. 179
7.5 Future Work and Open Problems	p. 185
7.6 Tutorial	p. 188
References	p. 190
8 Multiple Sequence Alignment in Parallel on a Cluster of Workstations	p. 193
8.1 Introduction	p. 193
8.2 CLUSTAL W	p. 194
8.3 Implementation	p. 201
8.4 Results	p. 207
8.5 Conclusion	p. 209
References	p. 210
9 Searching Sequence Databases Using High-Performance BLASTs	p. 211
9.1 Introduction	p. 211
9.2 Basic Blast Algorithm	p. 212
9.3 Blast Usage and Performance Factors	p. 214
9.4 High Performance BLASTs	p. 215
9.5 Comparing BLAST Performance	p. 221
9.6 UMD-BLAST	p. 226
9.7 Future Directions	p. 228
9.8 Related Work	p. 229
9.9 Summary	p. 230
References	p. 230
10 Parallel Implementations of Local Sequence Alignment: Hardware and Software	p. 233
10.1 Introduction	p. 233
10.2 Sequence Alignment Primer	p. 235
10.3 Smith-Waterman Algorithm	p. 240
10.4 FASTA	p. 244
10.5 BLAST	p. 245
10.6 HMMER - Hidden Markov Models	p. 249
10.7 ClustalW	p. 252
10.8 Specialized Hardware: FPGA	p. 257
10.9 Conclusion	p. 262
References	p. 262
11 Parallel Computing in the Analysis of Gene Expression Relationships	p. 265
11.1 Significance of Gene Expression Analysis	p. 265
11.2 Multivariate Gene Expression Relations	p. 267
11.3 Classification Based on Gene Expression	p. 274
11.4 Discussion and Future Directions	p. 280
References	p. 282
12 Assembling DNA Fragments with a Distributed Genetic Algorithm	p. 285
12.1 Introduction	p. 285
12.2 DNA Fragment Assembly Problem	p. 286
12.3 DNA Fragment Assembly Using the Sequential GA	p. 289
12.4 DNA Fragment Assembly Problem Using the Parallel GA	p. 292
12.5 Experimental Results	p. 294
12.6 Conclusions	p. 301
References	p. 301
13 A Cooperative Genetic Algorithm for Knowledge Discovery in Microarray Experiments	p. 303
13.1 Introduction	p. 303
13.2 Microarray Experiments	p. 304
13.3 Association Rules	p. 306
13.4 Multi-Objective Genetic Algorithm	p. 308
13.5 Cooperative Multi-Objective Genetic Algorithm (PMGA)	p. 313
13.6 Experiments	p. 315
13.7 Conclusion	p. 322
References	p. 322
Part III Phylogenetics	p. 325
14 Parallel and Distributed Computation of Large Phylogenetic Trees	p. 327
14.1 Introduction	p. 327
14.2 Maximum Likelihood	p. 330
14.3 State-of-the-Art ML Programs	p. 332
14.4 Algorithmic Solutions in RAxML-III	p. 334
14.5 HPC Solutions in RAxML-III	p. 337
14.6 Future Developments	p. 341
References	p. 344
15 Phylogenetic Parameter Estimation on COWs	p. 347
15.1 Introduction	p. 347
15.2 Phylogenetic Tree Reconstruction using Quartet Puzzling	p. 349
15.3 Hardware, Data, and Scheduling Algorithms	p. 354
15.4 Parallelizing PEst	p. 356
15.5 Extending Parallel Coverage in PEst	p. 359
15.6 Discussion	p. 365
References	p. 367
16 High-Performance Phylogeny Reconstruction Under Maximum Parsimony	p. 369
16.1 Introduction	p. 369
16.2 Maximum Parsimony	p. 374
16.3 Exact MP: Parallel Branch and Bound	p. 378
16.4 MP Heuristics: Disk-Covering Methods	p. 381
16.5 Summary and Open Problems	p. 390
References	p. 392
Part IV Protein Folding	p. 395
17 Protein Folding with the Parallel Replica Exchange Molecular Dynamics Method	p. 397
17.1 Introduction	p. 397
17.2 REMD Method	p. 399
17.3 Protein Folding with REMD	p. 403
17.4 Protein Structure Refinement with REMD	p. 420
17.5 Summary	p. 422
Reference	p. 423
18 High-Performance Alignment Methods for Protein Threading	p. 427
18.1 Introduction	p. 427
18.2 Formal Definition	p. 431
18.3 Mixed Integer Programming Models	p. 434
18.4 Divide-and-Conquer Technique	p. 444
18.5 Parallelization	p. 448
18.6 Future Research Directions	p. 453
18.7 Conclusion	p. 454
18.8 Summary	p. 454
References	p. 455
19 Parallel Evolutionary Computations in Discerning Protein Structures	p. 459
19.1 Introduction	p. 459
19.2 PSP Problem	p. 460
19.3 Protein Structure Discerning Methods	p. 461
19.4 PSP Energy Minimization EAs	p. 471
19.5 PSP Parallel EA Performance Evaluation	p. 477
19.6 Results and Discussion	p. 479
19.7 Conclusions and Suggested Research	p. 483
References	p. 483
Part V Platforms and Enabling Technologies	p. 487
20 A Brief Overview of Grid Activities for Bioinformatics and Health Applications	p. 489
20.1 Introduction	p. 489
20.2 Grid Computing	p. 490
20.3 Bioinformatics and Health Applications	p. 491
20.4 Grid Computing for Bioinformatics and Health Applications	p. 491
20.5 Grid Activities in Europe	p. 492
20.6 Grid Activities in the United Kingdom	p. 494
20.7 Grid Activities in the USA	p. 497
20.8 Grid Activities in Asia and Japan	p. 498
20.9 International Grid Collaborations	p. 499
20.10 International Grid Collaborations	p. 499
20.11 Conclusions and Future Trends	p. 500
References	p. 501
21 Parallel Algorithms for Bioinformatics	p. 509
21.1 Introduction	p. 509
21.2 Parallel Computer Architecture	p. 511
21.3 Bioinformatics Algorithms on the Cray MTA System	p. 517
21.4 Summary	p. 527
References	p. 528
22 Cluster and Grid Infrastructure for Computational Chemistry and Biochemistry	p. 531
22.1 Introduction	p. 531
22.2 GAMESS Execution on Clusters	p. 532
22.3 Portal Technology	p. 537
22.4 Running GAMESS with Nimrod Grid-Enabling Infrastructure	p. 538
22.5 Computational Chemistry Workflow Environments	p. 542
22.6 Conclusions	p. 546
References	p. 548
23 Distributed Workflows in Bioinformatics	p. 551
23.1 Introduction	p. 551
23.2 Challenges of Grid Computing	p. 553
23.3 Grid Applications	p. 554
23.4 Grid Programming	p. 555
23.5 Grid Execution Language	p. 557
23.6 GUI-Based Workflow Construction and Execution	p. 565
23.7 Case Studies	p. 570
23.8 Summary	p. 578
References	p. 579
24 Molecular Structure Determination on a Computational and Data Grid	p. 583
24.1 Introduction	p. 583
24.2 Molecular Structure Determination	p. 585
24.3 Grid Computing in Buffalo	p. 586
24.4 Center for Computational Research	p. 588
24.5 ACDC-Grid Overview	p. 588
24.6 Grid Research Collaborations	p. 596
24.7 Grid Research Advancements	p. 601
24.8 Grid Research Application Abstractions and Tools	p. 603
24.9 Conclusions	p. 616
References	p. 616
25 GIPSY: A Problem-Solving Environment for Bioinformatics Applications	p. 623
25.1 Introduction	p. 623
25.2 Architecture	p. 626
25.3 Currently Deployed Applications	p. 634
25.4 Conclusion	p. 647
References	p. 648
26 TaskSpaces: A Software Framework for Parallel Bioinformatics on Computational Grids	p. 651
26.1 Introduction	p. 651
26.2 The TaskSpaces Framework	p. 655
26.3 Application: Finding Correctly Folded RNA Motifs	p. 661
26.4 Case Study: Operating the Framework on a Computational Grid	p. 663
26.5 Results for the RNA Motif Problem	p. 664
26.6 Future Work	p. 668
26.7 Summary and Conclusion	p. 669
References	p. 669
27 The Organic Grid: Self-Organizing Computational Biology on Desktop Grids	p. 671
27.1 Introduction	p. 672
27.2 Background and Related Work	p. 674
27.3 Measurements	p. 686
27.4 Conclusions	p. 698
27.5 Future Directions	p. 699
References	p. 700
28 FPGA Computing in Modern Bioinformatics	p. 705
28.1 Parallel Processing Models	p. 706
28.2 Image Processing Task	p. 708
28.3 FPGA Hardware Accelerators	p. 711
28.4 Image Processing Example	p. 716
28.5 Case Study: Protein Structure Prediction	p. 720
28.6 Conclusion	p. 733
References	p. 734
29 Virtual Microscopy: Distributed Image Storage, Retrieval, Analysis, and Visualization	p. 737
29.1 Introduction	p. 737
29.2 Architecture	p. 738
29.3 Image Analysis	p. 747
29.4 Clinical Use	p. 752
29.5 Education	p. 755
29.6 Future Directions	p. 756
29.7 Summary	p. 759
References	p. 760
Index	p. 765

Available:*

On Order

Summary

Summary

Author Notes

Reviews 1

Choice Review

Table of Contents