Highly-sensitive measure of complexity captures boolean networks regimes and temporal order more optimally

TL;DR

This paper introduces a highly-sensitive complexity measure for Boolean networks that effectively detects chaotic regimes and elucidates state contributions to network dynamics, enhancing analysis of regulatory systems.

Contribution

The study develops and applies an algorithmic complexity measure that improves detection of chaos and state influence in Boolean networks, surpassing traditional methods.

Findings

Algorithmic Complexity reveals transitions to chaos more clearly.

States' contributions to transitions are effectively disclosed.

The measures are useful for analyzing regulatory network models.

Abstract

In this work, several random Boolean networks (RBN) are generated and analyzed from two characteristics: their time evolution diagram and their transition diagram. For this purpose, its randomness is estimated using three measures, of which Algorithmic Complexity is capable of both a) revealing transitions towards the chaotic regime in a more marked way, and b) disclosing the algorithmic contribution of certain states to the transition diagram and their relationship with the order they occupy in the temporal evolution of the respective RBN. The results obtained from both types of analysis are useful for the introduction of both Algorithmic Complexity and Perturbation Analysis in the context of Boolean networks, and their potential applications in regulatory network models.