Computational Molecular Biology: An Introduction / Edition 1

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More About This Textbook

Overview

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.
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Editorial Reviews

Booknews
A self-contained introduction for graduate or advanced undergraduate students of biology, computer science, statistics, or mathematics. Clote (computer science and biology, Boston College) and Backofen (U. of Munich) provide the background mathematics required to understand why certain algorithms work. They look at molecular biology, sequence alignment, evolution, hidden Markov models, and structural prediction. The CiP shows the subtitle as A Self-contained Approach to Bioinformatics. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher

"...much needed introductory level text on the subject..." (La Doc STI, July 2000)

"...very concise and compact..." (Mathematical Reviews, 2002h)

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Product Details

Table of Contents

Series Preface
Preface
1 Molecular Biology 1
1.1 Some Organic Chemistry 3
1.2 Small Molecules 4
1.3 Sugars 6
1.4 Nucleic Acids 6
1.5 Proteins 14
1.6 From DNA to Proteins 17
1.7 Exercises 21
2 Math Primer 23
2.1 Probability 23
2.2 Combinatorial Optimization 53
2.3 Entropy and Applications to Molecular Biology 61
2.4 Exercises 72
2.5 Appendix: Modification of Bezout's Lemma 77
3 Sequence Alignment 81
3.1 Motivating Example 83
3.2 Scoring Matrices 84
3.3 Global Pairwise Sequence Alignment 88
3.4 Multiple Sequence Alignment 111
3.5 Genomic Rearrangements 118
3.6 Locating Cryptogenes and Guide RNA 120
3.7 Expected Length of gRNA in Trypanosomes 123
3.8 Exercises 128
3.9 Appendix: Maximum-Likelihood Estimation for Pair Probabilities 132
4 All About Eve 135
4.1 Introduction 135
4.2 Rate of Evolutionary Change 137
4.3 Clustering Methods 144
4.4 Maximum Likelihood 157
4.5 Quartet Puzzling 166
4.6 Exercises 171
5 Hidden Markov Models 175
5.1 Likelihood and Scoring a Model 177
5.2 Re-estimation of Parameters 180
5.3 Applications 193
5.4 Exercises 197
6 Structure Prediction 201
6.1 RNA Secondary Structure 202
6.2 DNA Strand Separation 213
6.3 Amino Acid Pair Potentials 223
6.4 Lattice Models of Proteins 228
6.5 Hart and Istrial's Approximation Algorithm 234
6.6 Constraint-Based Structure Prediction 243
6.7 Protein Threading 246
6.8 Exercises 259
App. A Mathematical Background 263
App. B Resources 265
References 269
Index 281
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