Table of Contents

Preface; Part I: 1. A fly-over introduction; 2. Biological networks and graphs; 3. Character compatibility; 4. Near-cliques; 5. Parsimony in phylogenetics; 6. RNA folding; 7. Protein problems; 8. Tanglegrams; 9. TSP in genomics; 10. Molecular sequence analysis; 11. Metabolic networks and engineering; 12. ILP idioms; Part II: 13. Communities and cuts; 14. Corrupted data and extensions in phylogenetics; 15. More tanglegrams and trees; 16. Return to Steiner-trees; 17. Exploiting protein networks; 18. More strings and sequences; 19. Max-likelihood pedigrees; 20. Haplotyping; 21. Extended exercises; 22. What's next?; Epilogue: opinionated comments.

Promotional Information

This hands-on tutorial text for non-experts demonstrates biological applications of a versatile modeling and optimization technique.

About the Author

Dan Gusfield is Distinguished Professor of Computer Science at the University of California, Davis, and a Fellow of the IEEE, the ACM, and the International Society of Computational Biology (ISCB). His previous books include The Stable Marriage Problem (1989, with Robert W. Irving), Algorithms on Strings, Trees and Sequences (Cambridge, 1997) and ReCombinatorics (2014). He has served as chair of the computer science department at UCD (2000–04), and was the founding Editor-in-Chief of the IEEE/ACM Transactions of Computational Biology and Bioinformatics until January 2009. He has been instrumental in the definition and development of the intersection between computer science and computational biology.

Reviews

'In his classic accessible teaching style, Gusfield teaches us why integer linear programming (ILP) is the most useful mathematical idea you've probably never heard of. Read this book to learn how what you don't know can hurt you, and why ILP should be your new favorite method.' Trey Ideker, University of California, San Diego

'Once again, Dan Gusfield has written an accessible book that shows that algorithmic rigor need not be sacrificed when solving real-world problems. He explains integer linear programming in the context of real-world biology. In doing so, the reader has an enriched understanding of both algorithmic details and the challenges in modern biology.' Russ Altman, Stanford University, California