Computational Molecular Biology An Introduction

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Edition: 1st
Format: Hardcover
Pub. Date: 2000-10-03
Publisher(s): WILEY
List Price: $366.87

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Summary

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.

Author Biography

Peter Clote and Rolf Backofen are the authors of Computational Molecular Biology: An Introduction, published by Wiley.

Table of Contents

Series Preface xi
Preface xiii
Molecular Biology
1(22)
Some Organic Chemistry
3(1)
Small Molecules
4(2)
Sugars
6(1)
Nucleic Acids
6(8)
Nucleotides
6(2)
DNA
8(5)
RNA
13(1)
Proteins
14(3)
Amino Acids
14(1)
Protein Structure
15(2)
From DNA to Proteins
17(4)
Amino Acids and Proteins
17(2)
Transcription and Translation
19(2)
Exercises
21(2)
Acknowledgements and References
22(1)
Math Primer
23(58)
Probability
23(30)
Random Variables
25(2)
Some Important Probability Distributions
27(11)
Markov Chains
38(5)
Metropolis-Hastings Algorithm
43(4)
Markov Random Fields and Gibbs Sampler
47(5)
Maximum Likelihood
52(1)
Combinatorial Optimization
53(8)
Lagrange Multipliers
53(1)
Gradient Descent
54(1)
Heuristics Related to Simulated Annealing
54(1)
Applications of Monte Carlo
55(5)
Genetic Algorithms
60(1)
Entropy and Applications to Molecular Biology
61(11)
Information Theoretic Entropy
62(1)
Shannon Implies Boltzmann
63(3)
Simple Statistical Genomic Analysis
66(3)
Genomic Segmentation Algorithm
69(3)
Exercises
72(5)
Appendix: Modification of Bezout's Lemma
77(4)
Acknowledgements and References
79(2)
Sequence Alignment
81(54)
Motivating Example
83(1)
Scoring Matrices
84(4)
Global Pairwise Sequence Alignment
88(23)
Distance Methods
88(11)
Alignment with Tandem Duplication
99(11)
Similarity Methods
110(1)
Multiple Sequence Alignment
111(7)
Dynamic Programming
112(1)
Gibbs Sampler
112(2)
Maximum-Weight Trace
114(3)
Hidden Markov Models
117(1)
Steiner Sequences
117(1)
Genomic Rearrangements
118(2)
Locating Cryptogenes and Guide RNA
120(3)
Anchor and Periodicity Rules
122(1)
Search for Cryptogenes
122(1)
Expected Length of gRNA in Trypanosomes
123(5)
Exercises
128(4)
Appendix: Maximum-Likelihood Estimation for Pair Probabilities
132(3)
Acknowledgements and References
133(2)
All About Eve
135(40)
Introduction
135(2)
Rate of Evolutionary Change
137(7)
Amino Acid Sequences
137(2)
Nucleotide Sequences
139(5)
Clustering Methods
144(13)
Ultrametric Trees
147(5)
Additive Metric
152(4)
Estimating Branch Lengths
156(1)
Maximum Likelihood
157(9)
Likelihood of a Tree
159(1)
Recursive Definition for the Likelihood
160(2)
Optimal Branch Lengths for Fixed Topology
162(4)
Determining the Topology
166(1)
Quartet Puzzling
166(5)
Quartet Puzzling Step
169(1)
Majority Consensus Tree
170(1)
Exercises
171(4)
Acknowledgements and References
173(2)
Hidden Markov Models
175(26)
Likelihood and Scoring a Model
177(3)
Re-estimation of Parameters
180(13)
Baum--Welch Method
181(3)
EM and Justification of the Baum-Welch Method
184(3)
Baldi--Chauvin Gradient Descent
187(4)
Mamitsuka's MA Algorithm
191(2)
Applications
193(4)
Multiple Sequence Alignment
193(1)
Protein Motifs
194(1)
Eukaryotic DNA Promotor Regions
195(2)
Exercises
197(4)
Acknowledgements and References
198(3)
Structure Prediction
201(62)
RNA Secondary Structure
202(11)
DNA Strand Separation
213(10)
Amino Acid Pair Potentials
223(5)
Lattice Models of Proteins
228(6)
Monte Carlo and the Heteropolymer Protein Model
231(2)
Genetic Algorithm for Folding in the HP Model
233(1)
Hart and Istrial's Approximation Algorithm
234(9)
Performance
234(2)
Lower Bound
236(3)
Block Structure, Folding Point, and Balanced Cut
239(4)
Constraint-Based Structure Prediction
243(3)
Protein Threading
246(13)
Definition
246(3)
A Branch-and-Bound Algorithm
249(9)
NP-hardness
258(1)
Exercises
259(4)
Acknowledgements and References
261(2)
Appendix A Mathematical Background 263(2)
A.1 Asymptotic complexity
263(1)
A.2 Units of Measurement
263(1)
A.3 Lagrange Multipliers
264(1)
Appendix B Resources 265(4)
B.1 Web Sites
265(1)
B.2 The PDB Format
266(3)
References 269(12)
Index 281

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