Majority rules with random tie-breaking in Boolean gene regulatory networks.

We consider threshold boolean gene regulatory networks, where the update function of each gene is described as a majority rule evaluated among the regulators of that gene: it is turned ON when the sum of its regulator contributions is positive (activators contribute positively whereas repressors con...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Claudine Chaouiya, Ouerdia Ourrad, Ricardo Lima
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6279e382280149e2a2de756b27e09a07
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6279e382280149e2a2de756b27e09a07
record_format	dspace
spelling	oai:doaj.org-article:6279e382280149e2a2de756b27e09a072021-11-18T09:02:43ZMajority rules with random tie-breaking in Boolean gene regulatory networks.1932-620310.1371/journal.pone.0069626https://doaj.org/article/6279e382280149e2a2de756b27e09a072013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23922761/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203We consider threshold boolean gene regulatory networks, where the update function of each gene is described as a majority rule evaluated among the regulators of that gene: it is turned ON when the sum of its regulator contributions is positive (activators contribute positively whereas repressors contribute negatively) and turned OFF when this sum is negative. In case of a tie (when contributions cancel each other out), it is often assumed that the gene keeps it current state. This framework has been successfully used to model cell cycle control in yeast. Moreover, several studies consider stochastic extensions to assess the robustness of such a model. Here, we introduce a novel, natural stochastic extension of the majority rule. It consists in randomly choosing the next value of a gene only in case of a tie. Hence, the resulting model includes deterministic and probabilistic updates. We present variants of the majority rule, including alternate treatments of the tie situation. Impact of these variants on the corresponding dynamical behaviours is discussed. After a thorough study of a class of two-node networks, we illustrate the interest of our stochastic extension using a published cell cycle model. In particular, we demonstrate that steady state analysis can be rigorously performed and can lead to effective predictions; these relate for example to the identification of interactions whose addition would ensure that a specific state is absorbing.Claudine ChaouiyaOuerdia OurradRicardo LimaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e69626 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Claudine Chaouiya Ouerdia Ourrad Ricardo Lima Majority rules with random tie-breaking in Boolean gene regulatory networks.
description	We consider threshold boolean gene regulatory networks, where the update function of each gene is described as a majority rule evaluated among the regulators of that gene: it is turned ON when the sum of its regulator contributions is positive (activators contribute positively whereas repressors contribute negatively) and turned OFF when this sum is negative. In case of a tie (when contributions cancel each other out), it is often assumed that the gene keeps it current state. This framework has been successfully used to model cell cycle control in yeast. Moreover, several studies consider stochastic extensions to assess the robustness of such a model. Here, we introduce a novel, natural stochastic extension of the majority rule. It consists in randomly choosing the next value of a gene only in case of a tie. Hence, the resulting model includes deterministic and probabilistic updates. We present variants of the majority rule, including alternate treatments of the tie situation. Impact of these variants on the corresponding dynamical behaviours is discussed. After a thorough study of a class of two-node networks, we illustrate the interest of our stochastic extension using a published cell cycle model. In particular, we demonstrate that steady state analysis can be rigorously performed and can lead to effective predictions; these relate for example to the identification of interactions whose addition would ensure that a specific state is absorbing.
format	article
author	Claudine Chaouiya Ouerdia Ourrad Ricardo Lima
author_facet	Claudine Chaouiya Ouerdia Ourrad Ricardo Lima
author_sort	Claudine Chaouiya
title	Majority rules with random tie-breaking in Boolean gene regulatory networks.
title_short	Majority rules with random tie-breaking in Boolean gene regulatory networks.
title_full	Majority rules with random tie-breaking in Boolean gene regulatory networks.
title_fullStr	Majority rules with random tie-breaking in Boolean gene regulatory networks.
title_full_unstemmed	Majority rules with random tie-breaking in Boolean gene regulatory networks.
title_sort	majority rules with random tie-breaking in boolean gene regulatory networks.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/6279e382280149e2a2de756b27e09a07
work_keys_str_mv	AT claudinechaouiya majorityruleswithrandomtiebreakinginbooleangeneregulatorynetworks AT ouerdiaourrad majorityruleswithrandomtiebreakinginbooleangeneregulatorynetworks AT ricardolima majorityruleswithrandomtiebreakinginbooleangeneregulatorynetworks
_version_	1718420967281131520

Majority rules with random tie-breaking in Boolean gene regulatory networks.

Ejemplares similares