title: Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks.
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.
authors.affiliation: Departamento de Electromagnetismo y Física de la Materia e Instituto Carlos I de Física Teórica y Computacional. Universidad de Granada, E-18071 Granada, Spain.
authors.forename: Pablo
authors.initials: P
authors.lastname: Villegas
authors.affiliation: Departamento de Electromagnetismo y Física de la Materia e Instituto Carlos I de Física Teórica y Computacional. Universidad de Granada, E-18071 Granada, Spain.|Dipartimento di Fisica, Sapienza-Universitá di Roma, P.le A. Moro 5, 00185 Rome, Italy.
authors.forename: José
authors.initials: J
authors.lastname: Ruiz-Franco
authors.affiliation: Departamento de Electromagnetismo y Física de la Materia e Instituto Carlos I de Física Teórica y Computacional. Universidad de Granada, E-18071 Granada, Spain.|Dipartimento di Fisica 'G.Galilei' and CNISM, INFN, Universitá di Padova, Via Marzolo 8, 35131 Padova, Italy.
authors.forename: Jorge
authors.initials: J
authors.lastname: Hidalgo
authors.affiliation: Departamento de Electromagnetismo y Física de la Materia e Instituto Carlos I de Física Teórica y Computacional. Universidad de Granada, E-18071 Granada, Spain.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Muñoz
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D008957:Models, Genetic