Version 1
: Received: 25 March 2024 / Approved: 26 March 2024 / Online: 26 March 2024 (10:07:13 CET)
Version 2
: Received: 22 July 2024 / Approved: 23 July 2024 / Online: 24 July 2024 (07:24:36 CEST)
How to cite:
Luevano, M. D. J.; Puga, A. Highly-Sensitive Measure of Complexity Captures Boolean Networks Regimes and Temporal Order More Optimally. Preprints2024, 2024031569. https://doi.org/10.20944/preprints202403.1569.v2
Luevano, M. D. J.; Puga, A. Highly-Sensitive Measure of Complexity Captures Boolean Networks Regimes and Temporal Order More Optimally. Preprints 2024, 2024031569. https://doi.org/10.20944/preprints202403.1569.v2
Luevano, M. D. J.; Puga, A. Highly-Sensitive Measure of Complexity Captures Boolean Networks Regimes and Temporal Order More Optimally. Preprints2024, 2024031569. https://doi.org/10.20944/preprints202403.1569.v2
APA Style
Luevano, M. D. J., & Puga, A. (2024). Highly-Sensitive Measure of Complexity Captures Boolean Networks Regimes and Temporal Order More Optimally. Preprints. https://doi.org/10.20944/preprints202403.1569.v2
Chicago/Turabian Style
Luevano, M. D. J. and Alejandro Puga. 2024 "Highly-Sensitive Measure of Complexity Captures Boolean Networks Regimes and Temporal Order More Optimally" Preprints. https://doi.org/10.20944/preprints202403.1569.v2
Abstract
In this work, several random Boolean networks (RBN) are generated and analyzed from two characteristics: their time evolution diagram and their transition diagram. To do this, its randomness is estimated using three measures, of which Algorithmic Complexity is the only one capable of both a) revealing transitions towards the chaotic regime, 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.
Keywords
Random Boolean Networks; Entropy; Algorithmic Complexity; Compressibility
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
