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Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks
Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054426/ https://www.ncbi.nlm.nih.gov/pubmed/27713479 http://dx.doi.org/10.1038/srep34743 |
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author | Villegas, Pablo Ruiz-Franco, José Hidalgo, Jorge Muñoz, Miguel A. |
author_facet | Villegas, Pablo Ruiz-Franco, José Hidalgo, Jorge Muñoz, Miguel A. |
author_sort | Villegas, Pablo |
collection | PubMed |
description | Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent tradeoff between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way –even for asynchronous updating rules– and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity. |
format | Online Article Text |
id | pubmed-5054426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-50544262016-10-19 Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks Villegas, Pablo Ruiz-Franco, José Hidalgo, Jorge Muñoz, Miguel A. Sci Rep Article Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent tradeoff between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way –even for asynchronous updating rules– and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity. Nature Publishing Group 2016-10-07 /pmc/articles/PMC5054426/ /pubmed/27713479 http://dx.doi.org/10.1038/srep34743 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Villegas, Pablo Ruiz-Franco, José Hidalgo, Jorge Muñoz, Miguel A. Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks |
title | Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks |
title_full | Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks |
title_fullStr | Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks |
title_full_unstemmed | Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks |
title_short | Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks |
title_sort | intrinsic noise and deviations from criticality in boolean gene-regulatory networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054426/ https://www.ncbi.nlm.nih.gov/pubmed/27713479 http://dx.doi.org/10.1038/srep34743 |
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