Analysis of attractor distances in Random Boolean Networks

TL;DR

This paper investigates the distances between attractors in Random Boolean Networks, revealing distinct clustering patterns in ordered, chaotic, and critical networks through experimental analysis.

Contribution

It introduces three new distance measures for attractors and analyzes their properties across different network regimes, highlighting structural differences.

Findings

Ordered networks have highly clustered attractors.

Chaotic networks exhibit scattered attractor distributions.

Critical networks display mixed attractor patterns.

Abstract

We study the properties of the distance between attractors in Random Boolean Networks, a prominent model of genetic regulatory networks. We define three distance measures, upon which attractor distance matrices are constructed and their main statistic parameters are computed. The experimental analysis shows that ordered networks have a very clustered set of attractors, while chaotic networks' attractors are scattered; critical networks show, instead, a pattern with characteristics of both ordered and chaotic networks.