Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks

TL;DR

This study investigates how intrinsic noise influences the criticality of Boolean gene-regulatory networks, revealing that noise shifts optimal network operation from critical to subcritical states, especially for less complex tasks.

Contribution

It demonstrates that noise causes biological networks to operate subcritically, contrasting the traditional view that they function at criticality for optimal performance.

Findings

Quasi-critical networks learn complex tasks fastest.

Larger task complexity correlates with closer proximity to criticality.

Intrinsic noise shifts optimal network operation to subcritical regimes.

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 trade off 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.