Petri Net Representations in Metabolic Pathways
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Self-Modifying Nets, a Natural Extension of Petri Nets
Proceedings of the Fifth Colloquium on Automata, Languages and Programming
Discrete, Continuous, and Hybrid Petri Nets
Discrete, Continuous, and Hybrid Petri Nets
Neural system modeling and simulation using hybrid functional Petri net
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Neural system and ion channels remain one of the most intractable issues in biology over years because of its complexity. A representation that takes in both the intuition of biologists and the computational ability of the ion channel system is of great importance. In this paper, we exploit Hybrid Functional Petri net (HFPN) for representing ion channel dynamics. As an extension of Petri net, HFPN allows both discrete and continuous factors and realizes ordinary differential equations (ODE) which make it easy to handle biological factors in the ion channel system such as the open(close) state of ion channels and the influx (efflux) of various ions. We prove that neural elements can be naturally translated into HFPN. Simulation results of the action potential show our model very effective. Our work explores a novel approach for neuroscience research and a new application for Petri-net based method.