Dynamical regimes in non-ergodic random Boolean networks

TL;DR

This paper investigates the dynamical regimes of non-ergodic random Boolean networks, introducing new measures based on attractor properties to better predict system responses, with implications for biological and discrete models.

Contribution

It introduces a novel set of measures based on attractor properties to improve prediction of network behavior and responses, extending beyond traditional static or dynamic analyses.

Findings

New measures explain puzzling behaviors of networks

Attractor-based analysis improves prediction accuracy

Results applicable to various discrete models

Abstract

Random boolean networks are a model of genetic regulatory networks that has proven able to describe experimental data in biology. They not only reproduce important phenomena in cell dynamics, but they are also extremely interesting from a theoretical viewpoint, since it is possible to tune their asymptotic behaviour from order to disorder. The usual approach characterizes network families as a whole, either by means of static or dynamic measures. We show here that a more detailed study, based on the properties of system's attractors, can provide information that makes it possible to predict with higher precision important properties, such as system's response to gene knock-out. A new set of principled measures is introduced, that explains some puzzling behaviours of these networks. These results are not limited to random Boolean network models, but they are general and hold for any discrete model exhibiting similar dynamical characteristics.