Bioinformatics Technologies

Yi-Ping Phoebe Chen

  • 出版商: Springer
  • 出版日期: 2005-05-24
  • 售價: $1,150
  • 貴賓價: 9.5$1,093
  • 語言: 英文
  • 頁數: 396
  • 裝訂: Hardcover
  • ISBN: 3540208739
  • ISBN-13: 9783540208730

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Description

Solving modern biological problems requires advanced computational methods. Bioinformatics evolved from the active interaction of two fast-developing disciplines, biology and information technology. The central issue of this emerging field is the transformation of often distributed and unstructured biological data into meaningful information.

This book describes the application of well-established concepts and techniques from areas like data mining, machine learning, database technologies, and visualization techniques to problems like protein data analysis, genome analysis and sequence databases. Chen has collected contributions from leading researchers in each area. The chapters can be read independently, as each offers a complete overview of its specific area, or, combined, this monograph is a comprehensive treatment that will appeal to students, researchers, and R&D professionals in industry who need a state-of-the-art introduction into this challenging and exciting young field.

 

Table of contents


Preface ...................................................................................................V

1 Introduction to Bioinformatics............................................................1
1.1 Introduction ...................................................................................1
1.2 Needs of Bioinformatics Technologies...........................................2
1.3 An Overview of Bioinformatics Technologies................................5
1.4 A Brief Discussion on the Chapters................................................8
References.........................................................................................12

2 Overview of Structural Bioinformatics.............................................15
2.1 Introduction .................................................................................15
2.2 Organization of Structural Bioinformatics ....................................17
2.3 Primary Resource: Protein Data Bank ..........................................18
2.3.1 Data Format..........................................................................18
2.3.2 Growth of Data .....................................................................18
2.3.3 Data Processing and Quality Control.....................................20
2.3.4 The Future of the PDB..........................................................21
2.3.5 Visualization.........................................................................21
2.4 Secondary Resources and Applications ........................................22
2.4.1 Structural Classification ........................................................22
2.4.2 Structure Prediction ..............................................................28
2.4.3 Functional Assignments in Structural Genomics....................30
2.4.4 Protein-Protein Interactions...................................................32
2.4.5 Protein-Ligand Interactions ...................................................34
2.5 Using Structural Bioinformatics Approaches in Drug Design .......37
2.6 The Future...................................................................................39
2.6.1 Integration over Multiple Resources ......................................39
2.6.2 The Impact of Structural Genomics .......................................39
2.6.3 The Role of Structural Bioinformatics in Systems Biology ....39
References.........................................................................................40

3 Database Warehousing in Bioinformatics.........................................45
3.1 Introduction .................................................................................45
3.2 Bioinformatics Data.....................................................................48
3.3 Transforming Data to Knowledge ................................................51
3.4 Data Warehousing .......................................................................54
3.5 Data Warehouse Architecture.......................................................56
3.6 Data Quality ................................................................................58
3.7 Concluding Remarks....................................................................60
XII Contents
References.........................................................................................61

4 Data Mining for Bioinformatics ........................................................63
4.1 Introduction .................................................................................63
4.2 Biomedical Data Analysis............................................................64
4.2.1 Major Nucleotide Sequence Database, Protein Sequence
Database, and Gene Expression Database..............................65
4.2.2 Software Tools for Bioinformatics Research .........................68
4.3 DNA Data Analysis .....................................................................71
4.3.1 DNA Sequence .....................................................................71
4.3.2 DNA Data Analysis ..............................................................76
4.4 Protein Data Analysis ..................................................................92
4.4.1 Protein and Amino Acid Sequence ........................................92
4.4.2 Protein Data Analysis............................................................99
References.......................................................................................109

5 Machine Learning in Bioinformatics ..............................................117
5.1 Introduction ...............................................................................117
5.2 Artificial Neural Network ..........................................................120
5.3 Neural Network Architectures and Applications.........................128
5.3.1 Neural Network Architecture ..............................................128
5.3.2 Neural Network Learning Algorithms .................................131
5.3.3 Neural Network Applications in Bioinformatics ..................134
5.4 Genetic Algorithm.....................................................................135
5.5 Fuzzy System ............................................................................141
References.......................................................................................147

6 Systems Biotechnology: a New Paradigm in Biotechnology
Development ....................................................................................155
6.1 Introduction ...............................................................................155
6.2 Why Systems Biotechnology?....................................................156
6.3 Tools for Systems Biotechnology...............................................158
6.3.1 Genome Analyses ...............................................................158
6.3.2 Transcriptome Analyses ......................................................159
6.3.3 Proteome Analyses..............................................................161
6.3.4 Metabolome/Fluxome Analyses ..........................................163
6.4 Integrative Approaches ..............................................................164
6.5 In Silico Modeling and Simulation of Cellular Processes............166
6.5.1 Statistical Modeling ............................................................167
6.5.2 Dynamic Modeling .............................................................169
6.6 Conclusion ................................................................................170
References.......................................................................................171
Contents XIII

7 Computational Modeling of Biological Processes with Petri Net-
Based Architecture ..........................................................................179
7.1 Introduction ...............................................................................179
7.2 Hybrid Petri Net and Hybrid Dynamic Net.................................183
7.3 Hybrid Functional Petri Net .......................................................190
7.4 Hybrid Functional Petri Net with Extension ...............................191
7.4.1 Definitions ..........................................................................191
7.4.2 Relationships with Other Petri Nets.....................................197
7.4.3 Implementation of HFPNe in Genomic Object Net..............198
7.5 Modeling of Biological Processes with HFPNe ..........................198
7.5.1 From DNA to mRNA in Eucaryotes – Alternative Splicing .199
7.5.2 Translation of mRNA – Frameshift .....................................203
7.5.3 Huntington’s Disease ..........................................................203
7.5.4 Protein Modification – p53..................................................207
7.6 Related Works with HFPNe.......................................................211
7.7 Genomic Object Net: GON........................................................212
7.7.1 GON Features That Derived from HFPNe Features .............214
7.7.2 GON GUI and Other Features .............................................214
7.7.3 GONML and Related Works with GONML ........................220
7.7.4 Related Works with GON ...................................................222
7.8 Visualizer ..................................................................................224
7.8.1 Bio-processes on Visualizer ................................................226
7.8.2 Related Works with Visualizer ............................................231
7.9 BPE...........................................................................................233
7.10 Conclusion...............................................................................236
References.......................................................................................236

8 Biological Sequence Assembly and Alignment ...............................243
8.1 Introduction ...............................................................................243
8.2 Large-Scale Sequence Assembly................................................245
8.2.1 Related Research.................................................................245
8.2.2 Euler Sequence Assembly ...................................................249
8.2.3 PESA Sequence Assembly Algorithm.................................249
8.3 Large-Scale Pairwise Sequence Alignment ................................254
8.3.1 Pairwise Sequence Alignment .............................................254
8.3.2 Large Smith-Waterman Pairwise Sequence Alignment ........256
8.4 Large-Scale Multiple Sequence Alignment ................................257
8.4.1 Multiple Sequence Alignment .............................................257
8.4.2 Large-Scale Clustal W Multiple Sequence Alignment .........258
8.5 Load Balancing and Communication Overhead..........................259
8.6 Conclusion ................................................................................259
References.......................................................................................260
XIV Contents

9 Modeling for Bioinformatics ...........................................................263
9.1 Introduction ...............................................................................263
9.2 Hidden Markov Modeling for Biological Data Analysis .............264
9.2.1 Hidden Markov Modeling for Sequence Identification.........264
9.2.2 Hidden Markov Modeling for Sequence Classification ........273
9.2.3 Hidden Markov Modeling for Multiple Alignment
Generation ..........................................................................278
9.2.4 Conclusion..........................................................................280
9.3 Comparative Modeling ..............................................................281
9.3.1 Protein Comparative Modeling............................................281
9.3.2 Comparative Genomic Modeling.........................................284
9.4 Probabilistic Modeling...............................................................287
9.4.1 Bayesian Networks .............................................................287
9.4.2 Stochastic Context-Free Grammars .....................................288
9.4.3 Probabilistic Boolean Networks ..........................................288
9.5 Molecular Modeling ..................................................................290
9.5.1 Molecular and Related Visualization Applications...............290
9.5.2 Molecular Mechanics ..........................................................294
9.5.3 Modern Computer Programs for Molecular Modeling .........295
References.......................................................................................297

10 Pattern Matching for Motifs .........................................................299
10.1 Introduction .............................................................................299
10.2 Gene Regulation ......................................................................301
10.2.1 Promoter Organization ......................................................302
10.3 Motif Recognition....................................................................303
10.4 Motif Detection Strategies .......................................................305
10.4.1 Multi-genes, Single Species Approach ..............................306
10.5 Single Gene, Multi-species Approach.......................................307
10.6 Multi-genes, Multi-species Approach.......................................309
10.7 Summary .................................................................................309
References.......................................................................................310

11 Visualization and Fractal Analysis of Biological Sequences.........313
11.1 Introduction .............................................................................313
11.2 Fractal Analysis .......................................................................317
11.2.1 What Is a Fractal? .............................................................317
11.2.2 Recurrent Iterated Function System Model........................319
11.2.3 Moment Method to Estimate the Parameters of the IFS
(RIFS) Model....................................................................320
11.2.4 Multifractal Analysis.........................................................321
11.3 DNA Walk Models ..................................................................323
Contents XV
11.3.1 One-Dimensional DNA Walk............................................323
11.3.2 Two-Dimensional DNA Walk...........................................324
11.3.3 Higher-Dimensional DNA Walk .......................................325
11.4 Chaos Game Representation of Biological Sequences ..............325
11.4.1 Chaos Game Representation of DNA Sequences ...............325
11.4.2 Chaos Game Representation of Protein Sequences.............326
11.4.3 Chaos Game Representation of Protein Structures .............326
11.4.4 Chaos Game Representation of Amino Acid Sequences Based
on the Detailed HP Model............................................................327
11.5 Two-Dimensional Portrait Representation of DNA Sequences .330
11.5.1 Graphical Representation of Counters ...............................330
11.5.2 Fractal Dimension of the Fractal Set for a Given Tag.........332
11.6 One-Dimensional Measure Representation of Biological
Sequences................................................................................335
11.6.1 Measure Representation of Complete Genomes .................335
11.6.2 Measure Representation of Linked Protein Sequences .......340
11.6.3 Measure Representation of Protein Sequences Based on
Detailed HP Model............................................................344
References.......................................................................................348

12 Microarray Data Analysis .............................................................353
12.1 Introduction .............................................................................353
12.2 Microarray Technology for Genome Expression Study.............354
12.3 Image Analysis for Data Extraction..........................................356
12.3.1 Image Preprocessing .........................................................357
12.3.2 Block Segmentation ..........................................................359
12.3.3 Automatic Gridding ..........................................................360
12.3.4 Spot Extraction .................................................................360
12.3.5 Background Correction, Data Normalization and Filtering,
and Missing Value Estimation...........................................361
12.4 Data Analysis for Pattern Discovery.........................................363
12.4.1 Cluster Analysis ................................................................363
12.4.2 Temporal Expression Profile Analysis and Gene
Regulation ........................................................................371
12.4.3 Gene Regulatory Network Analysis...................................382
References.......................................................................................384

Index ...................................................................................................389