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  3. Integer Linear Programming in Computational and Systems Biology
  • Cited by 19
Publisher:
Cambridge University Press
Online publication date:
May 2019
Print publication year:
2019
Online ISBN:
9781108377737
DOI:
https://doi.org/10.1017/9781108377737
Subjects:
Genomics, Bioinformatics and Systems Biology, Life Sciences, Computer Science, Computational Biology and Bioinformatics
Information

Book description

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.

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

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Contents

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Contents

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