Ajuste automático de modelos celulares apoiado por algoritmos genéticos

Abstract

The modeling of the electrical activity of the heart is of great medical and scientific interest, because it provides a way for a better understanding of the related biophysical phenomena and it allows the development of new techniques for diagnoses, as well as new drugs. The current mathematical models are usually based on experimental data obtained from a small collection of cells. However, the electrical characteristics of cells vary along the heart. This heterogeneity plays a key role, but creates computational modeling difficulties. Currently, there are no global models capable of reproducing the electrical activity of different cells, even if neighbours. This work has as objective to evaluate a methodology based on Genetic Algorithms that aims to automatically adjust existing models of cellular electrophysiology to experimental data obtained from a cell or a collection of cardiac cells. The proposed methodology is implemented and evaluated through different numerical experiments. The models, adjusted by Genetic Algorithms, were able to reproduce the electrical activity measured by various in vitro experiments. Furthermore, the models generated by Genetic Algorithms were validated through new in vitro experiments using known effects of drugs which block specifie ion channels. Preliminary results suggest that the proposed methodology is a promising tool to support the development and use of cellular models.