Balance between noise and information flow maximizes set complexity of network dynamics.

ABSTRACT: Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from sub

SUBMITTER: Maki-Marttunen T 

PROVIDER: S-EPMC3596377 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature