Biology is in the midst of a era yielding many significant discoveries and promising many more. Unique to this era is the exponential growth in the size of information-packed databases. Inspired by a pressing need to analyze that data, Introduction to Computational Biology explores a new area of expertise that emerged from this fertile field- the combination of biological and information sciences.

This introduction describes the mathematical structure of biological data, especially from sequences and chromosomes. After a brief survey of molecular biology, it studies restriction maps of DNA, rough landmark maps of the underlying sequences, and clones and clone maps. It examines problems associated with reading DNA sequences and comparing sequences to finding common patterns. The author then considers that statistics of pattern counts in sequences, RNA secondary structure, and the inference of evolutionary history of related sequences.

Introduction to Computational Biology exposes the reader to the fascinating structure of biological data and explains how to treat related combinatorial and statistical problems. Written to describe mathematical formulation and development, this book helps set the stage for even more, truly interdisciplinary work in biology.

Computational Genome Analysis An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field.

This book features:

Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation

Presentation of fundamentals of probability, statistics, and algorithms

Implementation of computational methods with numerous examples based upon the R statistics package

Extensive descriptions and explanations to complement the analytical development

More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature

Exercises at the end of chapters

Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.

Simon Tavare holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavare's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavare is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.

Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels. "

This book presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book features a free download of the R software statistics package and the text provides great crossover material that is interesting and accessible to students in biology, mathematics, statistics and computer science. More than 100 illustrations and diagrams reinforce concepts and present key results from the primary literature. Exercises are given at the end of chapters.

As researchers have pursued biology's secrets to the molecular level, mathematical and computer sciences have played an increasingly important role?in genome mapping, population genetics, and even the controversial search for "Eve," hypothetical mother of the human race. In this first-ever survey of the partnership between the two fields, leading experts look at how mathematical research and methods have made possible important discoveries in biology. The volume explores how differential geometry, topology, and differential mechanics have allowed researchers to "wind" and "unwind" DNA's double helix to understand the phenomenon of supercoiling. It explains how mathematical tools are revealing the workings of enzymes and proteins. And it describes how mathematicians are detecting echoes from the origin of life by applying stochastic and statistical theory to the study of DNA sequences. This informative and motivational book will be of interest to researchers, research administrators, and educators and students in mathematics, computer sciences, and biology. Table of Contents Front Matter Chapter 1 The Secrets of Life: A Mathematician's Introduction to Molecular Biology BIOCHEMISTRY CLASSICAL GENETICS MOLECULAR BIOLOGY THE RECOMBINANT DNA REVOLUTION MOLECULAR GENETICS IN THE 1990S THE HUMAN GENOME PROJECT COMING ATTRACTIONS REFERENCES Chapter 2 Mapping Heredity: Using Probabilistic Models and Algorithms to Map Genes and Genomes The Concept of Genetic Maps Challenges of Genetic Mapping: Human Families and Complex Traits MAXIMUM LIKELIHOOD ESTIMATION Efficient Algorithms Excursion: Susceptibility to Colon Cancer in Mice and the Large Deviation Theory of Diffusion Processes Assembling Physical Maps by "Fingerprinting" Random Clones Excursion: Designing a Strategy to Map the Human Genome CONCLUSION REFERENCES Chapter 3 Seeing Conserved Signals: Using Algorithms to Detect Similarities between Biosequences FINDING GLOBAL SIMILARITIES Visualizing Alignments: Edit Graphs The Basic Dynamic Programming Algorithm FINDING LOCAL SIMILARITIES Variations in Gap Cost Penalties The Duality Between Similarity and Difference Measures Aligning More Than Two Sequences at a Time K-Best Alignments Approximate Pattern Matching Parallel Computing COMPARING ONE SEQUENCE AGAINST A DATABASE Heuristic Algorithms Sublinear Similarity Searches OPEN PROBLEMS REFERENCES Chapter 4 Hearing Distant Echoes: Using Extremal Statistics to Probe Evolutionary Origins Sequence Alignment Alignment Given Alignment Unknown Alignment Given Alignment Unknown APPLICATION TO RNA EVOLUTION TWO BEHAVIORS SUFFICE RNA EVOLUTION REVISITED REFERENCES Chapter 5 Calibrating the Clock: Using Stochastic Processes to Measure the Rate of Evolution OVERVIEW THE COALESCENT AND MUTATION The Ewens Sampling Formula Top-down Bottom-up The Infinitely-Many-Sites Model K-Allele Models The Finitely-Many-Sites Models Approximations for the Ewens Sampling Formula Combinatorial Assemblies Other Combinatorial Structures The Large Components WHERE TO NEXT? Likelihood Methods Discussion General-Purpose References Detailed References Chapter 6 Winding the Double Helix: Using Geometry, Topology, and Mechanics of DNA DNA GEOMETRY AND TOPOLOGY: LINKING, TWISTING, AND WRITHING APPLICATIONS TO DNA TOPOISOMERASE REACTIONS DNA ON PROTEIN COMPLEXES THE SURFACE LINKING NUMBER THE WINDING NUMBER AND HELICAL REPEAT RELATIONSHIP BETWEEN LINKING, SURFACE LINKING, AND WINDING APPLICATION TO THE STUDY OF THE MINICHROMOSOME REFERENCES Chapter 7 Unwinding the Double Helix: Using Differential Mechanics to Probe Conformational Changes... DNA SUPERHELICITY - MATHEMATICS AND BIOLOGY STATEMENT OF THE PROBLEM THE ENERGETICS OF A STATE ANALYSIS OF SUPERHELICAL EQUILIBRIA Evaluation of Free-Energy Parameters Accuracy of the Calculated Results APPLYING THE METHOD TO STUDY INTERESTING GENES DISCUSSION AND OPEN PROBLEMS REFERENCES Chapter 8 Lifting the Curtain: Using Topology to Probe the Hidden Action of Enzymes THE TOPOLOGY OF DNA SITE-SPECIFIC RECOMBINATION TOPOLOGICAL TOOLS FOR DNA ANALYSIS THE TANGLE MODEL FOR SITE-SPECIFIC RECOMBINATION THE TOPOLOGY OF TN3 RESOLVASE SOME UNSOLVED PROBLEMS Application of Geometry and Topology to Biology REFERENCES Chapter 9 Folding the Sheets: Using Computational Methods to Predict the Structure of Proteins A PRIMER ON PROTEIN STRUCTURE BASIC INSIGHTS ABOUT PROTEIN STRUCTURE THREADING METHODS PREDICTING HIV PROTEASE STRUCTURE:AN EXCURSION HIERARCHICAL APPROACHES PREDICTING MYOGLOBIN STRUCTURE:AN EXCURSION ACKNOWLEDGMENTS REFERENCES Appendix - Chapter Authors Index