Inferring bistable lac operen Boolean regulatory networks using evolutionary computation

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781467389884
DOI
EstadoPublicada - 4 oct. 2017
Publicado de forma externa
Evento2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017 - Manchester, Reino Unido
Duración: 23 ago. 201725 ago. 2017

Serie de la publicación

Nombre2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017

Conferencia

Conferencia2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
País/TerritorioReino Unido
CiudadManchester
Período23/08/1725/08/17

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