Integer Linear Programming in Computational and Systems Biology
An Entry-Level Text and Course
- Author: Dan Gusfield, University of California, Davis
Integer linear programming (ILP) is a versatile modeling and optimization technique that is increasingly used in non-traditional ways in biology, with the potential to transform biological computation. However, few biologists know about it. This how-to and why-do text introduces ILP through the lens of computational and systems biology. It uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP. This book aims to teach the logic of modeling and solving problems with ILP, and to teach the practical 'work flow' involved in using ILP in biology. Written for a wide audience, with no biological or computational prerequisites, this book is appropriate for entry-level and advanced courses aimed at biological and computational students, and as a source for specialists. Numerous exercises and accompanying software (in Python and Perl) demonstrate the concepts.Read more
- Presents the first-of-its-kind introduction to integer linear programming through the lens of computational and systems biology
- Includes 330 extended examples and exercises which mix logical thinking and practical application
- Accompanying software requires no previous computer programming knowledge and allows the reader to explore the material and develop skills in modeling and solving ILPs
Reviews & endorsements
'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 DiegoSee more reviews
'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
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- Date Published: June 2019
- format: Adobe eBook Reader
- isbn: 9781108389853
- contains: 123 b/w illus.
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
1. A fly-over introduction
2. Biological networks and graphs
3. Character compatibility
5. Parsimony in phylogenetics
6. RNA folding
7. Protein problems
9. TSP in genomics
10. Molecular sequence analysis
11. Metabolic networks and engineering
12. ILP idioms
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
21. Extended exercises
22. What's next?
Epilogue: opinionated comments.
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