Robustness Leads Close to the Edge of Chaos in Coupled Map Networks: toward the understanding of biological networks

TL;DR

This paper demonstrates that biological network models designed for robustness naturally evolve toward the edge of chaos, a state of marginal stability, through self-organization without fine-tuning.

Contribution

It shows that robustness in coupled map networks leads to the emergence of systems at the edge of chaos via self-organization, without parameter fine-tuning.

Findings

Systems with robustness against perturbations tend to be at the edge of chaos.

Edge of chaos systems emerge through self-organization.

Robustness is achieved without fine-tuning parameters.

Abstract

Dynamics in biological networks are in general robust against several perturbations. We investigate a coupled map network as a model motivated by gene regulatory networks and design systems which are robust against phenotypic perturbations (perturbations in dynamics), as well as systems which are robust against mutation (perturbations in network structure). To achieve such a design, we apply a multicanonical Monte Carlo method. Analysis based on the maximum Lyapunov exponent and parameter sensitivity shows that systems with marginal stability, which are regarded as systems at the edge of chaos, emerge when robustness against network perturbations is required. This emergence of the edge of chaos is a self-organization phenomenon and does not need a fine tuning of parameters.