Manuel de J. Luevano-Robledo, Alejandro Puga-Candelas

Physica D: Nonlinear Phenomena
Volume 482, November 2025, 134844

In this work, several random Boolean networks (RBNs) are generated and analyzed based on two fundamental features: their time evolution diagrams and their transition diagrams. For this purpose, we estimate randomness using three measures, among which Algorithmic Complexity stands out because it can (a) reveal transitions towards the chaotic regime more distinctly, and (b) disclose the algorithmic contribution of certain states to the transition diagrams, including their relationship with the order they occupy in the temporal evolution of the respective RBN. Results from both types of analysis illustrate the potential of Algorithmic Complexity and Perturbation Analysis for Boolean networks, paving the way for possible applications in modeling biological regulatory networks.

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