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E-raamat: In Silico Technologies in Drug Target Identification and Validation

Edited by (SciTegic, San Diego, California, USA), Edited by (SciTegic, San Diego, California, USA)
  • Formaat: 504 pages
  • Sari: Drug Discovery Series
  • Ilmumisaeg: 13-Jun-2006
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781420015737
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In Silico Technologies in Drug Target Identification and ValidationSuurem pilt
  • Formaat: 504 pages
  • Sari: Drug Discovery Series
  • Ilmumisaeg: 13-Jun-2006
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781420015737
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The pharmaceutical industry relies on numerous well-designed experiments involving high-throughput techniques and in silico approaches to analyze potential drug targets. These in silico methods are often predictive, yielding faster and less expensive analyses than traditional in vivo or in vitro procedures.

In Silico Technologies in Drug Target Identification and Validation addresses the challenge of testing a growing number of new potential targets and reviews currently available in silico approaches for identifying and validating these targets. The book emphasizes computational tools, public and commercial databases, mathematical methods, and software for interpreting complex experimental data. The book describes how these tools are used to visualize a target structure, identify binding sites, and predict behavior. World-renowned researchers cover many topics not typically found in most informatics books, including functional annotation, siRNA design, pathways, text mining, ontologies, systems biology, database management, data pipelining, and pharmacogenomics.

Covering issues that range from prescreening target selection to genetic modeling and valuable data integration, In Silico Technologies in Drug Target Identification and Validation is a self-contained and practical guide to the various computational tools that can accelerate the identification and validation stages of drug target discovery and determine the biological functionality of potential targets more effectively.

Daniel E. Levy, editor of the Drug Discovery Series, is the founder of DEL BioPharma, a consulting service for drug discovery programs. He also maintains a blog that explores organic chemistry.

Arvustused

The more chemists know about how biologistsidentify and validate drug targets, the more they can help ensure a successful project out come. The first 300 pages of this volume are dedicated to just these issues, reviewed from a practicing biologists perspective but described completely enough to provide useful insight for the medicinal chemist and pointing to online databases and tools that enterprising chemists might try using themselves. ... The remainder of the book covers some interesting emerging technologies, as well as computational infrastructure issues. ... ...The book is wide-ranging and yet practical in its review of in silico technologies, and the medicinal chemist will pick up useful background information in important target identification and validation techniques. The authors are clearly knowledgeable about their fields, and the editors have done an excellent job of melding this multi-authored book into a cohesive whole. ... -- Peter Gund, IBM Federal Healthcare Practice, Germantown, Maryland, USA (in the Journal of Medicinal Chemistry, 2007, Vol. 30, No. 9)

Chapter 1 Introduction 1(12)
Darryl Leon
1 The Drug-Development Landscape
1(3)
1.2 Historical Perspective
1(3)
3 Target Identification
4(1)
4 Target Validation
5(2)
5 Recent Trends
7(1)
6 Computational Infrastructure
7(1)
7 The Future of In Silico Technology
8(1)
References
9(4)
PART I Target Identification
Chapter 2 Pattern Matching
13(28)
Scott Markel and Vinodh N. Rajapakse
2.1 Introduction
14(1)
2.2 Historical Background
14(1)
2.3 Pattern Representation
15(1)
2.4 Databases
16(7)
2.4.1 Protein Patterns
16(1)
2.4.1.1 Motif/Domain
16(1)
2.4.1.2 Single Motif Patterns
17(1)
2.4.1.3 Multiple Motif Patterns
17(1)
2.4.1.4 Profile (HMM) Patterns
17(1)
2.4.1.5 Other
19(1)
2.4.1.6 Non-Interpro Pattern Repositories
19(1)
2.4.1.7 Protein Secondary Structure
20(1)
2.4.2 Nucleotide Patterns
21(1)
2.4.2.1 DDBJ/EMBL/GenBank Feature Table
21(1)
2.4.2.2 REBASE
22(1)
2.4.2.3 Repbase Update
23(1)
2.4.2.4 TRANSFAC
23(1)
2.5 Standards
23(1)
2.6 Tools
23(11)
2.6.1 Gene Finding
23(1)
2.6.1.1 GeneWise
24(1)
2.6.1.2 GFScan
25(1)
2.6.1.3 Genscan
25(1)
2.6.1.4 GeneMark
25(1)
2.6.1.5 FGENES, FGENESH
25(1)
2.6.1.6 HMMGene
26(1)
2.6.2 Protein Patterns
26(1)
2.6.2.1 Structural/Functional Motif Prediction
26(1)
2.6.2.2 Secondary-Structure Prediction
28(1)
2.6.3 Nucleotide Patterns
29(1)
2.6.3.1 RepeatMasker
29(1)
2.6.3.2 Splice-Site Prediction
29(1)
2.6.3.3 Primer Design
30(1)
2.6.4 EMBOSS
31(1)
2.6.5 GCG Wisconsin Package
31(1)
2.6.6 MEME/MAST/META-MEME
32(1)
2.6.6.1 MEME
32(1)
2.6.6.2 MAST
32(1)
2.6.6.3 META-MEME
32(1)
2.6.7 HMMER
33(1)
2.6.8 Write Your Own
33(1)
2.7 Future Directions
34(1)
2.7.1 Function Prediction in BioPatents
35(1)
2.7.2 Cell Penetrating Peptides
35(1)
Acknowledgments
35(1)
References
35(6)
Chapter 3 Tools for Computational Protein Annotation and Function Assignment
41(48)
Jaume M. Canaves
3.1 Introduction to Functional Annotation
42(2)
3.2 Sequence-Based Function Assignment
44(21)
3.2.1 Assigning Function by Direct Sequence Similarity
45(1)
3.2.2 Detection of Distant Similarities with Profile Methods
46(3)
3.2.3 Multiple Sequence Alignment
49(1)
3.2.3.1 Multiple Sequence Alignment Methods
50(1)
3.2.3.2 Integration of Multiple Sequence Alignments and Structural Data
51(1)
3.2.3.3 Analysis of Multiple Sequence Alignment Data
52(1)
3.2.3.4 Visualization and Edition of Multiple Sequence Alignments
53(2)
3.2.4 Functional Domain Identification
55(1)
3.2.4.1 Direct Domain Assignment through Search in Domain/Family Databases
55(1)
3.2.4.2 Domain Assignment through Indirect Evidence
57(1)
3.2.5 Function Assignments Based on Contextual Information
58(1)
3.2.5.1 Gene Fusions: The Rosetta Stone Method
58(1)
3.2.5.2 Domain Co-occurrence
60(1)
3.2.5.3 Genomic Context: Gene Neighborhoods, Gene Clusters, and Operons
60(1)
3.2.5.4 Phylogenomic Profiles
62(1)
3.2.5.5 Metabolic Reconstruction
63(1)
3.2.5.6 Protein–Protein Interactions
63(1)
3.2.5.7 Microarray Expression Profiles
64(1)
3.2.5.8 Other Sources of Contextual Information for Protein Annotation
64(1)
3.3 From Sequence to Structure: Homology and Ab Initio Structure Models
65(2)
3.4 Structure-Based Functional Annotation
67(2)
3.4.1 Structural Database Searches
67(1)
3.4.2 Structural Alignments
68(1)
3.4.3 Use of Structural Descriptors
68(1)
3.5 Final Remarks and Future Directions
69(3)
Acknowledgments
72(1)
References
72(10)
Links to Tools Mentioned in the Text
82(7)
Chapter 4 The Impact of Genetic Variation on Drug Discovery and Development
89(34)
Michael R. Barnes
4.1 Section 1
90(8)
4.1.1 Introduction
90(1)
4.1.2 Human Genetic Variation in a Drug-Discovery Context
91(2)
4.1.3 Forms and Mechanisms of Genetic Variation
93(1)
4.1.4 How Much Variation?
93(1)
4.1.5 Single Nucleotide Variation: SNPs and Mutations
94(1)
4.1.6 Functional Impact of SNPs and Mutations
94(1)
4.1.7 Candidate SNPs: When Is an SNP Not an SNP?
94(1)
4.1.8 VNTR Polymorphisms
95(1)
4.1.9 Insertion/Deletion Polymorphisms
96(1)
4.1.10 Genetics and the Search for Disease Alleles
97(1)
4.1.11 The Genome as a Framework for Data Integration of Genetic Variation Data
97(1)
4.2 Section 2
98(8)
4.2.1 Human Genetic Variation Databases and Web Resources
98(1)
4.2.2 Mutation Databases: An Avenue into Human Phenotype
98(1)
4.2.3 OMIM
98(2)
4.2.4 SNP Databases
100(1)
4.2.4.1 The dbSNP Database
100(1)
4.2.4.2 The RefSNP Dataset
100(1)
4.2.4.3 Searching dbSNP
100(1)
4.2.4.4 Human Genome Variation Database
102(1)
4.2.4.5 Evolution of SNP-Based Research and Technologies
103(1)
4.2.4.6 The SNP Consortium (TSC)
103(1)
4.2.4.7 JSNP—A Database of Japanese Single Nucleotide Polymorphisms
104(1)
4.2.5 The HapMap
104(1)
4.2.6 Defining Standards for SNP Data
105(1)
4.3 Section 3
106(4)
4.3.1 Tools for Visualization of Genetic Variation: The Genomic Context
106(1)
4.3.2 Tools for Visualization of Genetic Variation: The Gene Centric Context
107(1)
4.3.3 Entrez Gene and dbSNP Geneview
107(1)
4.3.4 SNPper
107(1)
4.3.5 GeneSNP
108(1)
4.3.6 Cancer Genome Annotation Project: Genetic Annotation Initiative
108(1)
4.3.7 SNP500Cancer
109(1)
4.3.8 Comparison of Consistency Across SNP Tools and Databases
109(1)
4.4 Section 4
110(8)
4.4.1 Determining the Impact of a Polymorphism on Gene and Target Function
110(1)
4.4.2 Principles of Predictive Functional Analysis of Polymorphisms
110(2)
4.4.3 A Decision Tree for Polymorphism Analysis
112(1)
4.4.4 The Anatomy of Promoter Regions and Regulatory Elements
113(2)
4.4.5 Gene Splicing
115(1)
4.4.6 Splicing Mechanisms, Human Disease, and Functional Analysis
115(1)
4.4.7 Functional Analysis of Polymorphisms in Putative Splicing Elements
116(1)
4.4.8 Functional Analysis on Nonsynonymous Coding Polymorphisms
117(1)
4.4.9 Integrated Tools for Functional Analysis of Genetic Variation
118(1)
4.4.9.1 PupaSNP and FastSNP
118(1)
4.5 Conclusions
118(1)
References
119(4)
Chapter 5 Mining of Gene-Expression Data
123(30)
Aedin Cuthane and Alvis Brazma
5.1 Introduction
123(1)
5.2 Preprocessing of Microarray Data
124(5)
5.3 Statistical Analysis of Microarray Data
129(1)
5.4 Eploratory Analysis
129(12)
5.4.1 Distance Measures
132(1)
5.4.2 Interpretation of Hierarchical Clustering Dendrograms and Eisen Heatmaps
133(1)
5.4.2.1 Assumptions and Limitations of Clustering
134(1)
5.4.3 Ordination: Visualization in a Reduced Dimension
135(1)
5.4.3.1 Interpretation of Plots from PCA or COA
138(3)
5.5 Supervised Classification and Class Prediction
141(1)
5.6 Target Identification: Gene Feature Selection
142(1)
5.7 Appraisal of Candidate Genes
143(1)
5.8 Meta-Analysis
144(1)
References
145(8)
PART II Target Validation
Chapter 6 Text Mining
153(42)
Bruce Gomes, William Hayes, and Raf M. Podowski
6.1 Introduction
154(2)
6.2 Technical Aspects of Text Mining
156(19)
6.2.1 Keyword Searching and Manual Methods
156(1)
6.2.1.1 Text Search
156(1)
6.2.1.2 Large-Scale Commercial Curation Efforts
157(1)
6.2.2 Literature Resources for Text Mining
158(1)
6.2.2.1 Abstract Collections
158(1)
6.2.2.2 Patents
159(1)
6.2.2.3 Full-Text Journal Access
159(1)
6.2.3 Ontology
160(1)
6.2.4 Text Categorization and Clustering
161(1)
6.2.4.1 Text Categorization
161(1)
6.2.4.2 Clustering
163(2)
6.2.5 Entity Extraction
165(1)
6.2.5.1 Gene Name Disambiguation
166(1)
6.2.5.2 Chemical Compound Entity Extraction
167(1)
6.2.6 Statistical Text Analyses
167(1)
6.2.7 Workflow Technologies
168(1)
6.2.8 NLP
169(3)
6.2.9 Agile NLP: Ontology-Based Interactive Information Extraction
172(3)
6.2.10 Visualization
175(1)
6.3 Examples of Text Mining
175(13)
6.3.1 Drug-Target Safety Assessment
176(3)
6.3.2 Landscape Map: Disease-to-Gene Linkages
179(1)
6.3.3 Applications of Text Mining in the Drug-Discovery and Development Process
180(2)
6.3.4 Systems Biology/Pathway Simulation
182(1)
6.3.5 Text Categorization
183(1)
6.3.6 Clustering: Literature Discovery
183(3)
6.4 Financial Value of Text Mining
186(2)
6.5 Discussion
188(2)
Acknowledgments
190(1)
References
190(5)
Chapter 7 Pathways and Networks
195(30)
Eric Minch and Ivayla Vatcheva
7.1 Introduction
196(1)
7.1.1 What Is a Pathway?
196(1)
7.1.2 What Are the Relationships among Different Sorts of Pathways?
196(1)
7.1.3 What Is the Significance of Pathways to Drug Discovery and Development?
197(1)
7.2 Pathway Data
197(7)
7.2.1 Data Acquisition Techniques
197(1)
7.2.1.1 Transcriptomics
198(1)
7.2.1.2 Proteomics
198(1)
7.2.1.3 Metabolomics
200(1)
7.2.2 Databases
200(1)
7.2.2.1 Primarily Metabolic Databases
200(1)
7.2.2.2 Signaling, Regulatory, and General Databases
201(2)
7.2.3 Standards
203(1)
7.3 Pathway Analysis
204(12)
7.3.1 Data Analysis Techniques
204(1)
7.3.1.1 Topological Analysis
204(1)
7.3.1.2 Flux Balance Analysis
206(1)
7.3.1.3 Metabolic Control Analysis
208(2)
7.3.2 Modeling
210(1)
7.3.2.1 Simulation
210(1)
7.3.2.2 Network Reconstruction
214(2)
7.4 Integrated Applications
216(2)
7.5 Future Directions
218(1)
References
218(7)
Chapter 8 Molecular Interactions: Learning from Protein Complexes
225(20)
Ana Rojas, David de Juan, and Alfonso Valencia
8.1 Molecular Interactions: Learning from Protein Complexes
225(1)
8.1.1 Molecular Interactions Are Essential to Understanding Biology
225(1)
8.2 Current Status of Experimental Procedures
226(2)
8.2.1 Reaching the Proteome: From Standard to Large-Scale Detection of Protein Interactions
226(1)
8.2.2 Structural Approaches
227(1)
8.3 The Range of Computational Methods
228(6)
8.3.1 Genomes, Sequences, and Domain Composition
229(1)
8.3.2 Structure: What Is Known about Interacting Surfaces?
230(1)
8.3.3 Predicting Structure from Protein Complexes: The Docking Problem
231(1)
8.3.4 Hybrid Methods Based on Sequence and Structure
232(2)
8.4 Merging Experimental and Computational Methods
234(1)
8.5 Where Is the Information?
235(1)
8.6 Perspectives
236(1)
Acknowledgements
237(1)
References
237(8)
Chapter 9 In Silico siRNA Design
245(16)
Darryl Leon
9.1 Introduction
245(3)
9.1.1 RNAi Biology
245(2)
9.1.2 siRNA Technology
247(1)
9.1 siRNA Design
248(4)
9.2.1 Designing an Optimized siRNA
248(3)
9.2.2 Selecting siRNA Targets
251(1)
9.2.3 siRNA and Sequence Similarity Searching
252(1)
9.3 Databases in siRNA
252(1)
9.4 siRNA Software
253(3)
9.4.1 Public Tools
253(2)
9.4.2 Commercial Efforts
255(1)
9.5 Practical Applications of siRNA
256(2)
9.5.1 Drug-Target Validation
256(1)
9.5.2 Functional Genomics
256(1)
9.5.3 Clinical Therapeutics
257(1)
Conclusion
258(1)
Acknowledgments
258(1)
References
258(3)
Chapter 10 Predicting Protein Subcellular Localization Using Intelligent Systems
261(24)
Rajesh Nair and Burkhard Rost
10.1 Introduction
262(2)
10.1.1 Decoding Protein Function: A Major Challenge for Modern Biology
262(1)
10.1.1.1 Protein Function Has Myriad Meanings
262(1)
10.1.1.2 What Makes Subcellular Localization Ideal for Function Prediction Experiments?
263(1)
10.1.1.3 Protein Trafficking Proceeds via Sorting Signals
264(1)
10.2 In Silico Approaches to Predicting Subcellular Localization
264(3)
10.2.1 No Straightforward Strategy for Predicting Localization
264(3)
10.3 Inferring Localization through Sequence Homology
267(1)
10.3.1 Most Annotations of Function through Homology Transfer
267(1)
10.3.2 LOChom: Database of Homology-Based Annotations
267(1)
10.4 Predicting Sequence Motifs Involved in Protein Targeting
268(3)
10.4.1 Prediction Possible for Some Cellular Classes
268(1)
10.4.2 TargetP: Predicting N-Terminal Signal Peptides
269(1)
10.4.3 PredictNLS: Predicting Nuclear Localization Signals
270(1)
10.5 Automatic Lexical Analysis of Controlled Vocabularies
271(2)
10.5.1 Mining Databases to Annotate Localization
271(1)
10.5.2 LOCkey: Information–Theory-Based Classifier
272(1)
10.6 Ab Initio Prediction from Sequence
273(2)
10.6.1 Ab Initio Methods Predict Localization for All Proteins at Lower Accuracy
273(1)
10.6.2 LOCnet: Improving Predictions Using Evolution
274(1)
10.7 Integrated Methods for Predicting Localization
275(2)
10.7.1 Improving Accuracy through Combinations
275(1)
10.7.2 PSORT II: Expert System for Predicting Localization
276(1)
10.8 Conclusion
277(1)
10.8.1 Several Pitfalls in Assessing Quality of Annotations
277(1)
10.8.2 Prediction Accuracy Continues to Grow
277(1)
Acknowledgments
278(1)
References
278(7)
Chapter 11 Three-Dimensional Structures in Target Discovery and Validation
285(24)
Sean I. O'Donoghue, Robert B. Russell, and Andrea Schafferhans
11.1 Introduction
286(1)
11.2 From Sequence to Structures
287(7)
11.2.1 How to Find Related Structures
287(3)
11.2.2 Which Structures to Choose
290(1)
11.2.2.1 Identical Sequences Are Not Always Equal
290(1)
11.2.2.2 Sequence Similarity is Best Guide—Usually!
291(1)
11.2.2.3 Complexes and Oligomers
291(1)
11.2.2.4 Differences Because of Experimental Method
292(1)
11.2.3 How to View 3D Structures
292(2)
11.3 From Structure to Function
294(6)
11.3.1 Using Structures in the Lab
295(1)
11.3.2 Finding Binding Sites
295(1)
11.3.2.1 Using Existing Annotations
296(1)
11.3.2.2 Using Structures Directly
296(1)
11.3.2.3 Using Sequence Profiles
296(1)
11.3.2.4 Using Structure Patterns
297(1)
11.3.3 Finding Function and Improving MSA
297(1)
11.3.4 Assessing Druggability
298(1)
11.3.5 Docking
299(1)
11.3.6 Comparing Structures and Binding Sites
300(1)
11.4 Discussion
300(1)
References
301(8)
PART III Recent Trends
Chapter 12 Comparative Genomics
309(14)
Viviane Siino, Bruce Pascal, and Christopher Sears
12.1 Introduction
309(1)
12.2 Infectious Disease
310(1)
12.3 Human Disorders
311(2)
12.4 Evolution
313(2)
12.5 Regulation and Pathways
315(1)
12.6 Agricultural Genomics
316(1)
12.7 Computations and Databases
317(1)
12.8 Conclusion
318(1)
References
319(4)
Chapter 13 Pharmacogenomics
323(22)
Bahrain Ghaffarzadeh Kermani
13.1 Introduction
324(2)
13.2 Case Studies
326(1)
13.2.1 P450 Family of Enzymes
326(1)
13.2.2 Heart Arrhythmia
326(1)
13.2.3 Breast Cancer
326(1)
13.2.4 Thiopurine Methyltransferase
327(1)
13.2.5 Alzheimer's Disease
327(1)
13.3 Related Technologies and Their Issues
327(5)
13.3.1 SNP Genotyping
327(1)
13.3.2 Haplotyping
328(1)
13.3.3 Linkage Disequilibrium
329(1)
13.3.4 Gene Expression
329(2)
13.3.5 Methylation
331(1)
13.3.6 Proteomics
332(1)
13.4 Expectations and Future Possibilities
332(2)
13.4.1 Resurrecting Previously Failed Drugs
332(1)
13.4.2 Balancing Efficacy and Toxicity of Drugs
333(1)
13.4.3 Improved Generalization
334(1)
13.5 Technical Challenges and Concerns
334(4)
13.5.1 Stationary and Global Genetic Information
334(1)
13.5.2 Disease Complexity and Mendelian Assumption
335(1)
13.5.3 Nature Versus Nurture
336(1)
13.5.4 The Cost of Medicine
336(1)
13.5.5 Clinical Trials
337(1)
13.6 Informatics Challenges
338(2)
13.6.1 Genotype/Phenotype Correlation
338(1)
13.6.2 Differential Gene Expression
338(1)
13.6.3 Differential Quantitative Genotyping
339(1)
13.6.4 Haplotype Map
339(1)
13.7 Discussion: Ethical Issues and Alternative Research
340(2)
13.7.1 Biases for Racial and Ethnic Groups
340(1)
13.7.2 Insurance
340(1)
13.7.3 Gender Differences
340(1)
13.7.4 Security of Genome Data Banks
341(1)
13.7.5 Biopsy from Healthy Tissues
341(1)
13.7.6 Diverting Attention from Alternative Research
341(1)
13.8 Conclusion
342(1)
Acknowledgments
342(1)
References
342(3)
Chapter 14 Target Identification and Validation Using Human Simulation Models
345(32)
Seth Michelson, Didier Scherrer, and Alex L. Bangs
14.1 Introduction
346(5)
14.1.1 Modeling: A Methodology for Idealizing a System
347(3)
14.1.2 Biosimulation: A Means of Characterizing the Solution Set of the Model
350(1)
14.2 The Challenge of Identifying and Validating a Drug Target
351(4)
14.2.1 Identifying the Key Biomolecular Entities Involved in a Disease's Pathophysiology
352(1)
14.2.2 The Context of the Biology—The Pathway
352(1)
14.2.3 The Logic of the Biology—The Dynamic Control Circuitry
353(1)
14.2.4 The Pressure Points of the System—Regulation of the Control Circuitry
353(1)
14.2.5 Patient Variability and Target Validation
354(1)
14.3 The Role of Predictive Biosimulation in Target Identification and Validation
355(4)
14.3.1 Capturing Patient Variability in the Biosimulation Milieu The Virtual Patient
355(1)
14.3.2 Applying Predictive Biosimulation to Target Identification
356(1)
14.3.2.1 Step 1: Define Target Functions and Assess Their Potential Clinical Impact
356(1)
14.3.2.2 Step 2: Modify the Model and Simulate Target Modulation
356(1)
14.3.2.3 Step 3: Analyze and Evaluate Biosimulation Results
357(2)
14.4 Case Studies
359(15)
14.4.1 Evaluating Novel Genes
360(1)
14.4.1.1 Creating Virtual Patients
360(1)
14.4.1.2 Hypothesis Generation and Prioritization of Gene Function
360(1)
14.4.1.3 Representation of Gene Function in Each Virtual Patient
361(1)
14.4.1.4 Hypothesis Testing through Simulation of Human Response
361(1)
14.4.1.5 Compare and Prioritize Data and Results
362(1)
14.4.1.6 Hypothesis Validation through Directed In Vitro! In Vivo Experiments
362(1)
14.4.2 Evaluating PDE4 as a Target for Asthma
363(1)
14.4.2.1 Characterization of PDE4 Roles in the Airways
364(1)
14.4.2.2 Virtual Patients
364(1)
14.4.2.3 Evaluating the Impact of PDE4 Inhibition on Clinical Outcome and Delineating Its Mechanism of Action
365(1)
14.4.3 Identifying Novel Targets in Rheumatoid Arthritis
365(1)
14.4.3.1 Sensitivity Analysis, Target Identification, and Quantification
366(1)
14.4.3.2 Simulation Results
367(1)
14.4.3.3 Reference Patient
368(1)
14.4.3.4 Mechanism of Action
371(3)
14.5 Conclusions
374(1)
References
374(3)
Chapter 15 Using Protein Targets for In Silico Structure-Based Drug Discovery
377(12)
Tad Hurst
15.1 Introduction
377(1)
15.2 Two-Dimensional Computer-Aided Drug Discovery
378(2)
15.3 Quantitative Structure Activity Relationships
380(1)
15.4 3D Searching Techniques
380(1)
15.5 Protein-Docking Techniques
381(4)
15.5.1 Docking Systems
382(1)
15.5.2 Accuracy
383(1)
15.5.3 Speed of Docking
384(1)
15.5.4 Binding Site Determination
384(1)
15.6 Conclusion
385(1)
References
385(4)
PART IV Computational Infrastructure
Chapter 16 Database Management
389(14)
Arek Kasprzyk and Damian Smedley
16.1 Introduction
389(1)
16.2 Biological Databases
390(1)
16.3 Data Integration
391(6)
16.3.1 Centralized Architecture
392(1)
16.3.1.1 Grand Unified Schema
392(1)
16.3.1.2 SeqHound
393(1)
16.3.1.3 Cancer Bioinformatics Infrastructure Objects
393(1)
16.3.1.4 Atlas
394(1)
16.3.2 Federated Architecture
394(1)
16.3.2.1 SRS
395(1)
16.3.2.2 K2
395(1)
16.3.2.3 Di scoveryLink
396(1)
16.3.2.4 BioMart
396(1)
16.4 Data Manipulation Software
397(3)
16.4.1 The Bio* Family
397(1)
16.4.2 GCG
398(1)
16.4.3 EMBOSS
399(1)
References
400(3)
Chapter 17 BioIT Hardware Configuration
403(8)
Philip Miller
17.1 Introduction
403(1)
17.2 Computer Hardware Systems
404(4)
17.2.1 BioIT Systems Design
404(2)
17.2.2 Cluster Computing
406(2)
17.2.3 Communications Network and Security
408(1)
17.3 LIMS, Material Tracking, and RFID
408(1)
17.4 Conclusions
409(1)
References
410(1)
Chapter 18 BioIT Architecture: Software Architecture for Bioinformatics Research
411(14)
Michael Dickson
18.1 A Definition of BioIT Architecture
411(1)
18.2 Requirements that Drive BioIT Architecture
412(2)
18.2.1 Integration of Public Versus Proprietary Data
412(1)
18.2.2 Compute-Intensive Analytical Algorithms
412(1)
18.2.3 Annotation of Knowledge onto Existing Data
412(1)
18.2.4 Information Sharing Across Project and Geographic Boundaries
413(1)
18.2.5 Ability to Quickly Adopt New Research Methods
413(1)
18.2.6 Manageability Built into the Infrastructure
413(1)
18.3 An Architecture that Realizes the Requirements
414(7)
18.3.1 High-Performance Computing and Computing on Demand
414(1)
18.3.2 Service-Oriented Architecture
415(6)
18.4 Modeling the Research Domain
421(2)
18.5 Summary
423(2)
Chapter 19 Workflows and Data Pipelines
425(26)
Michael Peeler
19.1 Introduction
426(3)
19.1.1 Workflows
426(1)
19.1.2 Data Pipelines
427(2)
19.1.3 Workflow Management Challenges
429(1)
19.2 Background
429(5)
19.2.1 Manual Workflows
430(1)
19.2.2 Simple Automation of Static Workflows
430(1)
19.2.3 Automated Workflow Engines
431(1)
19.2.4 Parallel Workflows
431(1)
19.2.5 Workflow Theory
432(1)
19.2.5.1 Petri Nets
432(1)
19.2.5.2 Workflow Patterns
432(2)
19.3 Tools
434(11)
19.3.1 Tools Overview
434(1)
19.3.2 Commercial Workflow Tools
434(1)
19.3.2.1 Incogen
435(1)
19.3.2.2 InforSense
436(1)
19.3.2.3 SciTegic
437(1)
19.3.2.4 TurboWorx
438(1)
19.3.2.5 White Carbon
440(1)
19.3.3 Open Source Workflow Tools
441(1)
19.3.3.1 Taverna
442(1)
19.3.3.2 Biopipe
443(1)
19.3.3.3 Other Open Source Workflow Tools
444(1)
19.4 Standards
445(2)
19.4.1 Organizations
445(1)
19.4.1.1 Workflow Management Coalition
445(1)
19.4.1.2 Business Process Management Initiative
446(1)
19.4.1.3 Object Management Group
446(1)
19.4.1.4 Organization for the Advancement of Structured Information Standards
446(1)
19.4.2 A Sampling of Workflow-Related Standards
446(1)
19.5 Future Trends and Challenges
447(1)
19.6 Conclusion
447(1)
References
448(3)
Petri Nets
448(1)
Workflows
448(1)
Workflow Patterns
448(1)
Data-Mining Tools
448(1)
Open Source Tools
449(1)
Standards-Related Publications
449(2)
Chapter 20 Ontologies
451(30)
Robin A. McEntire and Robert Stevens
20.1 Introduction
451(8)
20.1.1 What Is an Ontology?
453(2)
20.1.2 Ontologies from Knowledge Representation
455(3)
20.1.3 The Value of Ontologies
458(1)
20.2 The Current Environment for Ontologies
459(5)
20.2.1 Current Life Sciences Ontologies
460(2)
20.2.2 Ontology Tools
462(1)
20.2.3 Organizations Promoting Ontology Development
463(1)
20.3 Leveraging Ontologies for Drug-Target Identification and Validation
464(9)
20.3.1 Representing the Scientific Data and Information
465(3)
20.3.2 Integrating Information
468(1)
20.3.3 Workflow and Sharing Information within a Virtual Organization
469(2)
20.3.4 Text Mining and Ontologies
471(2)
20.4 Future Work
473(4)
20.4.1 Ontologies and Text Mining
473(1)
20.4.2 Ontology Standards
474(1)
20.4.3 Ontologies and Reasoning Systems
474(1)
20.4.4 Semantic Web
475(2)
20.5 Summary
477(1)
References
478(3)
Index 481


Darryl Leon, Scott Markel
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