Microdynamics and Criticality of Adaptive Regulatory Networks

TL;DR

This paper introduces a model of adaptive regulatory networks that self-organize into a critical state, exhibiting complex microdynamics and heavy-tailed degree distributions, driven by feedback loop formation and breaking.

Contribution

It provides an analytically supported model demonstrating how adaptive rewiring leads to criticality in regulatory networks.

Findings

Networks exhibit heavy-tailed degree distributions.

Networks self-organize to a critical state.

Criticality arises from feedback loop dynamics.

Abstract

We present a model of adaptive regulatory networks consisting of a simple biologically-motivated rewiring procedure coupled to an elementary stability criterion. The resulting networks exhibit a characteristic stationary heavy-tailed degree distribution, show complex structural microdynamics and self-organize to a dynamically critical state. We show analytically that the observed criticality results from the formation and breaking of transient feedback loops during the adaptive process.