Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks Villegas, Pablo Ruiz-Franco, José Hidalgo Aguilera, Jorge Muñoz Martínez, Miguel Ángel Gene regulatory networks Boolean networks Bioinformatics Gene expression Noise 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. 2017-02-10T12:21:29Z 2017-02-10T12:21:29Z 2016 info:eu-repo/semantics/article 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] 2045-2322 http://hdl.handle.net/10481/44773 10.1038/srep34743 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Nature Publishing Group