Modified Oregonator: an Approach from the Complex Networks Theory

Keywords: Systems Biology, BZ Reaction, Complex Networks, Supervised Learning, Gramian Angular Field

Abstract

Within the framework of Systems Biology, this paper proposes the complex network theory as a fundamental tool for determining the most critical dynamic variables in complex biochemical mechanisms. The Belousov-Zhabotinsky reaction is proposed as a study model and as a complex bipartite network. By determining the structural property authority, the most relevant dynamic variables are specified, and a mathematical model of the Belousov-Zhabotinsky reaction is obtained. The bidirectional coupling of the proposed model was made with other models associated with biological processes, finding synchronization phenomena when varying the coupling parameter. The time series obtained from the numerical solution of the coupled models were used to construct their images using the Gramian Angular Field technique. In the end, a supervised learning tool is proposed for the classification of the type of coupling by analyzing the images, obtaining score percentages above 94%. The hereby proposed methodology could be extended to the experimental field in order to determine anomalies in the coupling and synchronization of different physiological oscillators.

References

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Published
2020-09-01
How to Cite
Rojas, J. F., Arzola, J. A., & Vidal Robles, E. (2020). Modified Oregonator: an Approach from the Complex Networks Theory. Mexican Journal of Biomedical Engineering, 41(3), 6-27. Retrieved from http://rmib.mx/index.php/rmib/article/view/1034
Section
Research Articles