Neural system modeling and simulation using hybrid functional Petri net

Abstract

The Petri net formalism has been proved to be a powerful method in biological modeling. It not only boasts of a most intuitive graphical and network presentation, but also combines the methods of classical systems biology with discrete modeling technique. Hybrid Functional Petri net (HFPN) was proposed specially for biological system modeling in 2003. An array of well constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. Files of our model and more results with their analysis are available at: http://www.iipl.fudan.edu.cn/staff/Webpage_ACM.htm