Modeling Signal Transduction of Neural System by Hybrid Petri Net Representation

Shih Chi Peng (), Hsu-Ming Chang (), D. Frank Hsu () and Chuan Yi Tang ()
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Shih Chi Peng: National Tsing Hua University
Hsu-Ming Chang: National Tsing Hua University
D. Frank Hsu: Fordham University
Chuan Yi Tang: National Tsing Hua University

A chapter in Operations Research Proceedings 2004, 2005, pp 271-279 from Springer

Abstract: Abstract Biological neural system can be considered as a series of biochemical reactions and signal transmission. It is important to provide an intuitive representation of the neural system to biologists while keeping its computational consistency. In this paper, we propose a method to exploit Hybrid Petri Net (HPN) for intuitive representation and quantitative modeling. The HPN is an extension of Petri Nets and represented by a directed, bipartite graph in which nodes are either discrete/continuous places (such as ion channels) or discrete/continuous transitions (such as phosphorylation), where places represent conditions and transitions represent activities. It can easily model the interactions among receptors, ionic flows (such as calcium), G-proteins, protein kinases and transcription factors that are very complicate in terms of the dynamics of all participants and their correlations. We demonstrate that, in the biological neural system, it is possible to translate and map these complex phenomena into HPNs in a natural manner. In our model, the dynamic properties of the neural signal processing can be examined, especially the interactions among neural modulators and signal transduction pathways. With such a mechanism model in hand, our ability to collaborate with neural scientists is greatly enhanced so as to simulate and examine the robustness of the neural transmission under the local biochemical perturbations.

Keywords: Neural System; Speed Function; Intuitive Representation; Discrete Place; Biological Neural System (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-540-27679-1_34

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DOI: 10.1007/3-540-27679-3_34

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