Inferring bistable lac operen Boolean regulatory networks using evolutionary computation

Gonzalo A. Ruz, Daniel Ashlock, Thomas Ledger, Eric Goles

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The lac operen in E. coli is one of the earliest examples of an inducible system of genes being under both positive and negative control that is capable of showing bistability. In this paper, we present a methodology to infer synthetic threshold Boolean regulatory networks of a reduced model of the lac operon using evolutionary computation. The formulation consists in a vector representation of the solutions (networks) and a fitness function specially designed to correctly simulate the bistability through the models' fixed points. We compared the effectiveness and efficiency (runtime) of the proposed approach using three evolutionary computation techniques: differential evolution, genetic algorithms, and particle swarm optimization. The results showed that the three algorithms are capable of finding solutions, being differential evolution the most effective, whereas genetic algorithms was the least effective and efficient in terms of runtime. Particle swarm optimization obtained a good trade-off between effectiveness versus efficiency. One of the inferred solutions was analyzed showing some interesting biological insights, as well as correctly being able to model bistability without any spurious attractors. Overall, the proposed formulation was effective to infer bistable lac operon models under the threshold Boolean network paradigm.

Original languageEnglish
Title of host publication2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467389884
DOIs
StatePublished - 4 Oct 2017
Externally publishedYes
Event2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017 - Manchester, United Kingdom
Duration: 23 Aug 201725 Aug 2017

Publication series

Name2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017

Conference

Conference2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
Country/TerritoryUnited Kingdom
CityManchester
Period23/08/1725/08/17

Fingerprint

Dive into the research topics of 'Inferring bistable lac operen Boolean regulatory networks using evolutionary computation'. Together they form a unique fingerprint.

Cite this