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

Gonzalo A. Ruz, Tania Timmermann, Eric Goles

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

7 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2016 IEEE Congress on Evolutionary Computation, CEC 2016
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas4281-4288
Número de páginas8
ISBN (versión digital)9781509006229
DOI
EstadoPublicada - 14 nov. 2016
Evento2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canadá
Duración: 24 jul. 201629 jul. 2016

Serie de la publicación

Nombre2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conferencia

Conferencia2016 IEEE Congress on Evolutionary Computation, CEC 2016
País/TerritorioCanadá
CiudadVancouver
Período24/07/1629/07/16

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