Inference of threshold network models of the tryptophan operon in Escherichia coli

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

Abstract

A recent Boolean model of the tryptophan operon in Escherichia coli has been introduced, which exhibits a desired set of fixed points modeling the operon state being on or off. Nevertheless, when updated synchronously, the model also reveals spurious limit cycles with a larger basin of attraction than the desired fixed points. This paper presents the search via evolutionary computation for threshold network models that exhibit the desired fixed points without any limit cycles. The proposed framework was applied using differential evolution and particle swarm optimization, the latter being the most efficient and effective method based on the results obtained by the simulations. Particle swarm optimization found two correct threshold networks, one of which has only weight values -1,0,1, making the model more interpretable.

Original languageEnglish
Title of host publicationCIBCB 2023 - 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350310177
DOIs
StatePublished - 2023
Externally publishedYes
Event20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2023 - Eindhoven, Netherlands
Duration: 29 Aug 202331 Aug 2023

Publication series

NameCIBCB 2023 - 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

Conference

Conference20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2023
Country/TerritoryNetherlands
CityEindhoven
Period29/08/2331/08/23

Keywords

  • Boolean network
  • Differential evolution
  • Evolutionary Computation
  • Particle swarm optimization
  • Threshold network
  • Tryptophan operon

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