Conference Paper

Integer Linear Programming in Computational and Systems Biology

2018 International Conference on Bioinformatics and Computational Biology
Dan Gus eld


Integer (Linear) Programming, abbreviated

\ILP", is a versatile modeling and optimization

technique that was developed for complex planning

and operational decision making. However, it has

been increasingly used in computational biology

in non-traditional ways, most importantly and

inventively as a computational tool to model

biological phenomena, to analyze biological data,

and to extract biological insight from the models

and the data. Integer programming is often very

eective in solving instances of hard biological

problems on realistic data of current importance,

despite the fact that many of those problems lack

general algorithmic solutions that are ecient (in a

provable, worst-case sense), and that the problem

of solving integer programs also lacks a provable

worst-case ecient general solution.

Highly engineered, commercial ILP solvers are

available (now free to academics and researchers)

to solve ILP formulations. The improvement of the

best solvers has been spectacular, with an estimate

that (combined with faster computers) benchmark

ILP problems can now be solved 200-billion times

faster than twenty-ve years ago. Exploiting ILP,

some biological problems of importance can be

modeled in a way that allows a solution in seconds

on a laptop, while more common (statistically-

based) models require days, weeks or months of

computation on large clusters.

The eectiveness of the best ILP solvers on

problem instances of importance in biology opens

huge opportunities. The impact of faster and easier-

to-implement computation could be truly transfor-

mative in several parts of biology. However, there

are challenges in eectively using these tools for

biological problems, and educational and outreach

issues that must be addressed. In this talk, I will

discuss some of the successes, opportunities, and

challenges in exploiting ILP for computational and

systems biology.

BICOB 2018

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