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

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...

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Vydané v:Physica. D Ročník 482; s. 134844
Hlavní autori: de J. Luevano-Robledo, Manuel, Puga-Candelas, Alejandro
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.11.2025
Predmet:
Algorithmic complexity
Compressibility
Entropy
Perturbation analysis
Random Boolean networks
Algorithmic complexity
Entropy
Compressibility
Perturbation analysis
Random Boolean networks
ISSN:0167-2789
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Shrnutí: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. [Display omitted] •BDM classifies chaotic RBN regimes with high accuracy, surpassing entropy/LZW.•Perturbation analysis shows BDM sensitivity to node centrality, unlike entropy/LZW.•BDM detects RBN phase transitions, emphasizing its utility for dynamical systems analysis.
ISSN:0167-2789
DOI:10.1016/j.physd.2025.134844