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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/44773

Title: Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks
Authors: Villegas, Pablo
Ruiz-Franco, José
Hidalgo Aguilera, Jorge
Muñoz Martínez, Miguel Ángel
Issue Date: 2016
Abstract: 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.
Sponsorship: Spanish-MINECO grant FIS2013-43201-P (FEDER funds) for financial support
Publisher: Nature Publishing Group
Keywords: Gene regulatory networks
Boolean networks
Gene expression
URI: http://hdl.handle.net/10481/44773
ISSN: 2045-2322
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Villegas, P.; et al. Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks. Scientific Reports, 6: 34743 (2016). [http://hdl.handle.net/10481/44773]
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