Parallel Computing for Bioinformatics and Computational Biology: Models, Enabling Technologies, and Case Studies (Hardcover)
暫譯: 生物資訊學與計算生物學的平行計算：模型、啟用技術與案例研究（精裝版）
Albert Y. Zomaya
- 出版商: Wiley
- 出版日期: 2006-04-01
- 售價: $7,620
- 貴賓價: 9.5 折 $7,239
- 語言: 英文
- 頁數: 816
- 裝訂: Hardcover
- ISBN: 0471718483
- ISBN-13: 9780471718482
-
相關分類:
生物資訊 Bioinformatics
海外代購書籍(需單獨結帳)
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商品描述
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
Preface.
Contributors.
Acknowledgments.
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 Escherichia coli (Christophe Chassagnole).
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 (K. Burrage).
4.1 Introduction.
4.2 Simulation Methods for Stochastic Chemical Kinetics.
4.3 Aspects of Biology— 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 KangI0.
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 Biological 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 FutureWork and Open Problems.
7.6 Tutorial.
References.
8 Multiple Sequence Alignment in Parallel on a Cluster ofWorkstations (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 RelatedWork.
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 DNA Fragment Assembly Using the Sequential GA.
12.4 DNA Fragment Assembly Problem Using the 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 Phylogenetic Tree Reconstruction using Quartet Puzzling.
15.3 Hardware, Data, and Scheduling Algorithms.
15.4 Parallelizing PEst.
15.5 Extending Parallel Coverage in PEst.
15.6 Discussion.
References.
16 High-Performance Phylogeny Reconstruction Under Maximum Parsimony (Tiffani L. Williams).
16.1 Introduction.
16.2 Maximum Parsimony.
16.3 Exact MP: Parallel Branch and Bound.
16.4 MP Heuristics: Disk-Covering Methods.
16.5 Summary and Open Problems.
References.
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 and Health Applications.
20.5 Grid Activities in Europe.
20.6 Grid Activities in the United Kingdom.
20.7 Grid Activities in the USA.
20.8 Grid Activities in Asia and Japan.
20.9 International Grid Collaborations.
20.10 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-Enabling Infrastructure.
22.5 Computational ChemistryWorkflow Environments.
22.6 Conclusions.
References.
23 DistributedWorkflows in Bioinformatics (Arun Krishnan).
23.1 Introduction.
23.2 Challenges of Grid Computing.
23.3 Grid Applications.
23.4 Grid Programming.
23.5 Grid Execution Language.
23.6 GUI-BasedWorkflow Construction and Execution.
23.7 Case Studies.
23.8 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 Grid Research Collaborations.
24.7 Grid Research Advancements.
24.8 Grid Research Application Abstractions and Tools.
24.9 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: Finding Correctly Folded RNA Motifs.
26.4 Case Study: Operating the Framework on a Computational Grid.
26.5 Results for the RNA Motif Problem.
26.6 FutureWork.
26.7 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 RelatedWork.
27.3 Measurements.
27.4 Conclusions.
27.5 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 Image Processing Example.
28.5 Case Study: Protein Structure Prediction.
28.6 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.
References.
Index.
商品描述(中文翻譯)
**描述**
發現如何利用平行計算簡化複雜的生物資訊學應用。
這本出版物使讀者能夠處理更複雜的生物資訊學應用以及更大且更豐富的數據集。正如編輯所清楚顯示的，使用強大的平行計算工具可以在解碼基因組、理解遺傳疾病、設計定制藥物療法以及理解進化方面帶來重大突破。
涵蓋了廣泛的生物資訊學應用，並展示了如何將每一個應用進行平行化以提高性能並獲得更快的計算速度。當前的平行計算技術和技術，包括分散式計算和網格計算，均有探討。讀者將獲得一系列算法、實驗和模擬，這些不僅提供定性見解，還提供定量見解，深入了解生物資訊學這一動態領域。
《生物資訊學與計算生物學的平行計算》是一部貢獻作品，作為案例研究的資料庫，集體展示了平行計算如何簡化生物資訊學中的困難問題並產生更好的結果。每一章均由該領域的知名專家撰寫，並經過仔細編輯，以確保整本出版物的一致性和高標準。
**本書分為五個部分：**
- 算法與模型
- 序列分析與微陣列
- 系統發育學
- 蛋白質摺疊
- 平台與啟用技術
生物資訊學領域的研究人員、教育工作者和學生將發現高效能計算如何使他們能夠處理更複雜的數據集，獲得更深入的見解，並進行新的發現。
**目錄**
前言
貢獻者
致謝
第一部分：算法與模型
1 平行與進化方法在計算生物學中的應用（Nouhad J. Rizk）
1.1 介紹
1.2 生物資訊學
1.3 應用於計算生物學的進化計算
1.4 結論
參考文獻
2 平行蒙地卡羅模擬HIV分子進化以應對免疫監視（Jack da Silva）
2.1 介紹
2.2 問題
2.3 模型
2.4 使用MPI進行平行化
2.5 平行隨機數生成
2.6 初步模擬結果
2.7 未來方向
參考文獻
3 用於大腸桿菌中糖解作用和戊糖磷酸途徑的體內動態分析的差分進化算法（Christophe Chassagnole）
3.1 介紹
3.2 數學模型
3.3 模型參數的估計
3.4 通過DE進行動力學參數估計
3.5 模擬與結果
3.6 穩定性分析
3.7 控制特性
3.8 結論
參考文獻
4 用於細胞模型的計算密集型模擬（K. Burrage）
4.1 介紹
4.2 隨機化學動力學的模擬方法
4.3 生物學的各個方面—基因調控
4.4 生物系統的平行計算
4.5 平行模擬
4.6 細胞系統的空間建模
4.7 模擬細胞群落
參考文獻
5 在人腦中模擬擴散和變形的平行計算（Ning Kang）
5.1 介紹
5.2 白質纖維追蹤中的各向異性擴散模擬
5.3 在影像引導神經外科中的腦變形模擬
5.4 總結
參考文獻
第二部分：序列分析與微陣列
6 計算分子生物學（Azzedine Boukerche）
6.1 介紹
6.2 分子生物學的基本概念
6.3 全局與局部生物序列比對
6.4 生物序列比較的啟發式方法
6.5 平行與分散式序列比較
6.6 結論
參考文獻
7 用於生物序列分析的專用計算（Bertil Schmidt）
7.1 介紹
7.2 混合平行計算機
7.3 動態編程通信模式
7.4 性能評估
7.5 未來工作與開放問題
7.6 教學
參考文獻
8 在工作站集群上進行平行的多序列比對（Amitava Datta）
8.1 介紹
8.2 CLUSTALW
8.3 實現
8.4 結果
8.5 結論
參考文獻
9 使用高效能BLAST搜索序列數據庫（Xue Wu）
9.1 介紹
9.2 基本BLAST算法
9.3 BLAST的使用與性能因素
9.4 高效能BLAST
9.5 比較BLAST性能
9.6 UMD-BLAST
9.7 未來方向
9.8 相關工作
9.9 總結
參考文獻
10 局部序列比對的平行實現：硬體與軟體（Vipin Chaudhary）
10.1 介紹
10.2 序列比對入門
10.3 Smith–Waterman算法
10.4 FASTA
10.5 BLAST
10.6 HMMER—隱馬可夫模型
10.7 ClustalW
10.8 專用硬體：FPGA
10.9 結論
參考文獻
11 在基因表達關係分析中的平行計算（Robert L. Martino）
11.1 基因表達分析的重要性
11.2 多變量基因表達關係
11.3 基於基因表達的分類
11.4 討論與未來方向
參考文獻
12 使用分散式遺傳算法組裝DNA片段（Gabriel Luque）
12.1 介紹
12.2 DNA片段組裝問題
12.3 使用序列GA的DNA片段組裝
12.4 使用平行GA的DNA片段組裝問題
12.5 實驗結果
12.6 結論
參考文獻
13 用於微陣列實驗的知識發現的合作遺傳算法（Mohammed Khabzaoui）
13.1 介紹
13.2 微陣列實驗
