Enhanced descriptions from Syndetics:

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.

Table of contents provided by Syndetics

• Series Preface (p. xi)
• Preface (p. xiii)
• 1 Molecular Biology (p. 1)
• 1.1 Some Organic Chemistry (p. 3)
• 1.2 Small Molecules (p. 4)
• 1.3 Sugars (p. 6)
• 1.4 Nucleic Acids (p. 6)
• 1.4.1 Nucleotides (p. 6)
• 1.4.2 DNA (p. 8)
• 1.4.3 RNA (p. 13)
• 1.5 Proteins (p. 14)
• 1.5.1 Amino Acids (p. 14)
• 1.5.2 Protein Structure (p. 15)
• 1.6 From DNA to Proteins (p. 17)
• 1.6.1 Amino Acids and Proteins (p. 17)
• 1.6.2 Transcription and Translation (p. 19)
• 1.7 Exercises (p. 21)
• Acknowledgments and References (p. 22)
• 2 Math Primer (p. 23)
• 2.1 Probability (p. 23)
• 2.1.1 Random Variables (p. 25)
• 2.1.2 Some Important Probability Distributions (p. 27)
• 2.1.3 Markov Chains (p. 38)
• 2.1.4 Metropolis-Hastings Algorithm (p. 43)
• 2.1.5 Markov Random Fields and Gibbs Sampler (p. 47)
• 2.1.6 Maximum Likelihood (p. 52)
• 2.2 Combinatorial Optimization (p. 53)
• 2.2.1 Lagrange Multipliers (p. 53)
• 2.2.2 Gradient Descent (p. 54)
• 2.2.3 Heuristics Related to Simulated Annealing (p. 54)
• 2.2.4 Applications of Monte Carlo (p. 55)
• 2.2.5 Genetic Algorithms (p. 60)
• 2.3 Entropy and Applications to Molecular Biology (p. 61)
• 2.3.1 Information Theoretic Entropy (p. 62)
• 2.3.2 Shannon Implies Boltzmann (p. 63)
• 2.3.3 Simple Statistical Genomic Analysis (p. 66)
• 2.3.4 Genomic Segmentation Algorithm (p. 69)
• 2.4 Exercises (p. 72)
• 2.5 Appendix: Modification of Bezout's Lemma (p. 77)
• Acknowledgements and References (p. 79)
• 3 Sequence Alignment (p. 81)
• 3.1 Motivating Example (p. 83)
• 3.2 Scoring Matrices (p. 84)
• 3.3 Global Pairwise Sequence Alignment (p. 88)
• 3.3.1 Distance Methods (p. 88)
• 3.3.2 Alignment with Tandem Duplication (p. 99)
• 3.3.3 Similarity Methods (p. 110)
• 3.4 Multiple Sequence Alignment (p. 111)
• 3.4.1 Dynamic Programming (p. 112)
• 3.4.2 Gibbs Sampler (p. 112)
• 3.4.3 Maximum-Weight Trace (p. 114)
• 3.4.4 Hidden Markov Models (p. 117)
• 3.4.5 Steiner Sequences (p. 117)
• 3.5 Genomic Rearrangements (p. 118)
• 3.6 Locating Cryptogenes and Guide RNA (p. 120)
• 3.6.1 Anchor and Periodicity Rules (p. 122)
• 3.6.2 Search for Cryptogenes (p. 122)
• 3.7 Expected Length of gRNA in Trypanosomes (p. 123)
• 3.8 Exercises (p. 128)
• 3.9 Appendix: Maximum-Likelihood Estimation for Pair Probabilities (p. 132)
• Acknowledgements and References (p. 133)
• 4 All About Eve (p. 135)
• 4.1 Introduction (p. 135)
• 4.2 Rate of Evolutionary Change (p. 137)
• 4.2.1 Amino Acid Sequences (p. 137)
• 4.2.2 Nucleotide Sequences (p. 139)
• 4.3 Clustering Methods (p. 144)
• 4.3.1 Ultrametric Trees (p. 147)
• 4.3.2 Additive Metric (p. 152)
• 4.3.3 Estimating Branch Lengths (p. 156)
• 4.4 Maximum Likelihood (p. 157)
• 4.4.1 Likelihood of a Tree (p. 159)
• 4.4.2 Recursive Definition for the Likelihood (p. 160)
• 4.4.3 Optimal Branch Lengths for Fixed Topology (p. 162)
• 4.4.4 Determining the Topology (p. 166)
• 4.5 Quartet Puzzling (p. 166)
• 4.5.1 Quartet Puzzling Step (p. 169)
• 4.5.2 Majority Consensus Tree (p. 170)
• 4.6 Exercises (p. 171)
• Acknowledgements and References (p. 173)
• 5 Hidden Markov Models (p. 175)
• 5.1 Likelihood and Scoring a Model (p. 177)
• 5.2 Re-estimation of Parameters (p. 180)
• 5.2.1 Baum-Welch Method (p. 181)
• 5.2.2 EM and Justification of the Baum-Welch Method (p. 184)
• 5.2.3 Baldi-Chauvin Gradient Descent (p. 187)
• 5.2.4 Mamitsuka's MA Algorithm (p. 191)
• 5.3 Applications (p. 193)
• 5.3.1 Multiple Sequence Alignment (p. 193)
• 5.3.2 Protein Motifs (p. 194)
• 5.3.3 Eukaryotic DNA Promotor Regions (p. 195)
• 5.4 Exercises (p. 197)
• Acknowledgements and References (p. 198)
• 6 Structure Prediction (p. 201)
• 6.1 RNA Secondary Structure (p. 202)
• 6.2 DNA Strand Separation (p. 213)
• 6.3 Amino Acid Pair Potentials (p. 223)
• 6.4 Lattice Models of Proteins (p. 228)
• 6.4.1 Monte Carlo and the Heteropolymer Protein Model (p. 231)
• 6.4.2 Genetic Algorithm for Folding in the HP Model (p. 233)
• 6.5 Hart and Istrial's Approximation Algorithm (p. 234)
• 6.5.1 Performance (p. 234)
• 6.5.2 Lower Bound (p. 236)
• 6.5.3 Block Structure, Folding Point, and Balanced Cut (p. 239)
• 6.6 Constraint-Based Structure Prediction (p. 243)
• 6.7 Protein Threading (p. 246)
• 6.7.1 Definition (p. 246)
• 6.7.2 A Branch-and-Bound Algorithm (p. 249)
• 6.7.3 NP-hardness (p. 258)
• 6.8 Exercises (p. 259)
• Acknowledgements and References (p. 261)
• Appendix A Mathematical Background (p. 263)
• A.1 Asymptotic complexity (p. 263)
• A.2 Units of Measurement (p. 263)
• A.3 Lagrange Multipliers (p. 264)
• Appendix B Resources (p. 265)
• B.1 Web Sites (p. 265)
• B.2 The PDB Format (p. 266)
• References (p. 269)
• Index (p. 281)

Author notes provided by Syndetics

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