Computational Molecular Biology: An Introduction / Edition 1

Computational Molecular Biology: An Introduction / Edition 1

by Peter Clote, Rolf Backofen
     
 

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ISBN-10: 0471872512

ISBN-13: 9780471872511

Pub. Date: 10/11/2000

Publisher: Wiley

Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions

Overview

Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.
* Provides the background mathematics required to understand why certain algorithms work
* Guides the reader through probability theory, entropy and combinatorial optimization
* In-depth coverage of molecular biology and protein structure prediction
* Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction
* Includes class tested exercises useful for self study
* Source code of programs available on a Web site Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields.

Product Details

ISBN-13:
9780471872511
Publisher:
Wiley
Publication date:
10/11/2000
Series:
Wiley Series in Mathematical & Computational Biology Series, #1
Pages:
300
Product dimensions:
6.81(w) x 10.04(h) x 0.91(d)

Table of Contents

Series Prefacexi
Prefacexiii
1Molecular Biology1
1.1Some Organic Chemistry3
1.2Small Molecules4
1.3Sugars6
1.4Nucleic Acids6
1.4.1Nucleotides6
1.4.2DNA8
1.4.3RNA13
1.5Proteins14
1.5.1Amino Acids14
1.5.2Protein Structure15
1.6From DNA to Proteins17
1.6.1Amino Acids and Proteins17
1.6.2Transcription and Translation19
1.7Exercises21
Acknowledgments and References22
2Math Primer23
2.1Probability23
2.1.1Random Variables25
2.1.2Some Important Probability Distributions27
2.1.3Markov Chains38
2.1.4Metropolis-Hastings Algorithm43
2.1.5Markov Random Fields and Gibbs Sampler47
2.1.6Maximum Likelihood52
2.2Combinatorial Optimization53
2.2.1Lagrange Multipliers53
2.2.2Gradient Descent54
2.2.3Heuristics Related to Simulated Annealing54
2.2.4Applications of Monte Carlo55
2.2.5Genetic Algorithms60
2.3Entropy and Applications to Molecular Biology61
2.3.1Information Theoretic Entropy62
2.3.2Shannon Implies Boltzmann63
2.3.3Simple Statistical Genomic Analysis66
2.3.4Genomic Segmentation Algorithm69
2.4Exercises72
2.5Appendix: Modification of Bezout's Lemma77
Acknowledgements and References79
3Sequence Alignment81
3.1Motivating Example83
3.2Scoring Matrices84
3.3Global Pairwise Sequence Alignment88
3.3.1Distance Methods88
3.3.2Alignment with Tandem Duplication99
3.3.3Similarity Methods110
3.4Multiple Sequence Alignment111
3.4.1Dynamic Programming112
3.4.2Gibbs Sampler112
3.4.3Maximum-Weight Trace114
3.4.4Hidden Markov Models117
3.4.5Steiner Sequences117
3.5Genomic Rearrangements118
3.6Locating Cryptogenes and Guide RNA120
3.6.1Anchor and Periodicity Rules122
3.6.2Search for Cryptogenes122
3.7Expected Length of gRNA in Trypanosomes123
3.8Exercises128
3.9Appendix: Maximum-Likelihood Estimation for Pair Probabilities132
Acknowledgements and References133
4All About Eve135
4.1Introduction135
4.2Rate of Evolutionary Change137
4.2.1Amino Acid Sequences137
4.2.2Nucleotide Sequences139
4.3Clustering Methods144
4.3.1Ultrametric Trees147
4.3.2Additive Metric152
4.3.3Estimating Branch Lengths156
4.4Maximum Likelihood157
4.4.1Likelihood of a Tree159
4.4.2Recursive Definition for the Likelihood160
4.4.3Optimal Branch Lengths for Fixed Topology162
4.4.4Determining the Topology166
4.5Quartet Puzzling166
4.5.1Quartet Puzzling Step169
4.5.2Majority Consensus Tree170
4.6Exercises171
Acknowledgements and References173
5Hidden Markov Models175
5.1Likelihood and Scoring a Model177
5.2Re-estimation of Parameters180
5.2.1Baum-Welch Method181
5.2.2EM and Justification of the Baum-Welch Method184
5.2.3Baldi-Chauvin Gradient Descent187
5.2.4Mamitsuka's MA Algorithm191
5.3Applications193
5.3.1Multiple Sequence Alignment193
5.3.2Protein Motifs194
5.3.3Eukaryotic DNA Promotor Regions195
5.4Exercises197
Acknowledgements and References198
6Structure Prediction201
6.1RNA Secondary Structure202
6.2DNA Strand Separation213
6.3Amino Acid Pair Potentials223
6.4Lattice Models of Proteins228
6.4.1Monte Carlo and the Heteropolymer Protein Model231
6.4.2Genetic Algorithm for Folding in the HP Model233
6.5Hart and Istrial's Approximation Algorithm234
6.5.1Performance234
6.5.2Lower Bound236
6.5.3Block Structure, Folding Point, and Balanced Cut239
6.6Constraint-Based Structure Prediction243
6.7Protein Threading246
6.7.1Definition246
6.7.2A Branch-and-Bound Algorithm249
6.7.3NP-hardness258
6.8Exercises259
Acknowledgements and References261
Appendix AMathematical Background263
A.1Asymptotic complexity263
A.2Units of Measurement263
A.3Lagrange Multipliers264
Appendix BResources265
B.1Web Sites265
B.2The PDB Format266
References269
Index281

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