Synopsis
Computational biology applies the power of computers to large, complex mathematical problems arising in molecular biology, especially in DNA sequencing. Setubal and Meidanis provide an overview of algorithms for computational biology as well as basic information regarding major problems. Readers interested in a particular problem will find background material on molecular biology, definition 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 asymptotic running time with better algorithms, the contrast between heuristics and an algorithm with guarantees, and the difficulty posed by NP-complete problems.
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From the Back Cover
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.
Review
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
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Introduction to Computational Molecular Biology
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