Neural system modeling and simulation using Hybrid Functional Petri Net.
The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. 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 the 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. The file of our model and more results with their analysis are available in our supplementary material.
Shanghai Key Lab of Intelligent Information Processing, Fudan University, 220 Handan Road, Shanghai 200433, China. tangyin831009@gmail.com
Yin
Y
Tang
Fei
F
Wang
D000818:Animals; D003198:Computer Simulation; D051379:Mice; D008954:Models, Biological; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009638:Norepinephrine / Q000378:metabolism; D051381:Rats; D009435:Synaptic Transmission
D009638:Norepinephrine