Until now, those interested in the emerging field of computational molecular biology have used surveys and technical articles collected from many sources. Introduction to Computational Molecular Biology brings together major results in the field, in coherent and readable format. Setubal and Meidanis present a representative sample of problems in molecular biology, focusing on the algorithms that have been proposed to solve them. Readers will find background material on molecular biology, definitions of key terms, descriptions of models, and a full sample of algorithmic results. Key theoretical computer science concepts are emphasized, such as the improvement in asymtotic running time with better algorithms, the contrast between heuristics and an algorithm with guarantees, and the difficulty posed by NP-complete problems. Algorithms for sequence comparison, including the popular BLAST and FAST programs, are covered. Introduction to Computational Molecular Biology serves readers from both the mathematical and computing sciences as well as molecular biology. The authors assume a basic chemistry background and some training in college-level discrete mathematics and algorithms.
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Preface 1. Basic Concepts of Molecular Biology 2. Strings, Graphs, and Algorithms 3. Sequence Comparison and Database Search 4. Fragment Assembly of DNA 5. Physical Mapping of DNA 6. Phylogenetic Trees 7. Genome Rearrangements 8. Molecular Structure Prediction 9. Epilogue: Computing with DNA Answers to Selected Exercises / References / Index
The exploding number and complexity of problems faced by DNA researchers and scientists have driven the application of mathematical and computer science in molecular biology. This textbook provides an introduction to computational molecular biology, a sampling of representative problems, and examples of efficient algorithms that have been proposed to solve them. Illuminating each problem is a discussion of its biological background, definition of key terms, a complete description of the mathematical or computer science model used, and a sample of algorithmic results. As a result, this book serves as a valuable resource for computer scientists and mathematicians seeking to improve their understanding of molecular biology and its computational aspects, for students taking a course on the subject, and for researchers looking for a broad overview of this exciting new field, where many algorithmic problems still lay unexplored.
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