Neutral space analysis of gene regulatory network models of salt stress response in Arabidopsis using evolutionary computation

Gonzalo A. Ruz, Tania Timmermann, Eric Goles

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

6 Scopus citations

Abstract

Boolean networks are popular models to represent gene regulatory networks due to their simplicity and capacity to give an initial idea of the qualitative dynamics of a gene regulatory network represented by the temporal evolution of the protein states. In this paper, we analyze the neutral space of Boolean network models of salt stress response in Arabidopsis through the construction of neutral networks. To infer Boolean networks to build the neutral network, we use an evolution strategy that uses a wildtype network to generate initial candidate solutions. We compare the neutral space results when we consider two different wildtypes. Our results show the effectiveness and usefulness of the evolutionary computation approach for this problem, as well as findings related to how the neutral space is shaped depending of the initial wildtype employed as well as particular characteristics of the evolution strategy used in this work.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4281-4288
Number of pages8
ISBN (Electronic)9781509006229
DOIs
StatePublished - 14 Nov 2016
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

Name2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conference

Conference2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16

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