Highly-sensitive measure of complexity captures boolean networks regimes and temporal order more optimally
<|reference_start|>Highly-sensitive measure of complexity captures boolean networks regimes and temporal order more optimally: 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.<|reference_end|>
@article{luevano2024highly-sensitive,
    title={Highly-sensitive measure of complexity captures boolean networks regimes
  and temporal order more optimally},
    author={Manuel de J. Luevano, Alejandro Puga},
    journal={arXiv preprint arXiv:2409.00319},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00319},
    primaryClass={cs.IT math.IT}
}