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.
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References
J.W. Pinney, D.R. Westhead and G.A. McConkey (2003) Petri Net representations in systems biology. Biochemical Society Transactions Vol. 31, part 6, pp 1513–1515
H. Matsuno, Y. Tanaka, H. Aoshima, A. Doi, M. Matsui and S. Miyano (2003) Biopathways representation and simulation on hybrid functional Petri net. In Silico Biology 3, 0032.
M.C. Kohn and D.R. Lemieux (1991) Identification of regulatory properties of metabolic networks by graph theoretical modeling. Journal of Theoretical Biology, Vol.150, pp 3–25
P.A. Meric and MJ. Wise (1998) Quantitative scalable discrete-event simulation of metabolic pathway. Intelligent System for Molecular Biology, AAAI Press, pp. 187–194
H. Matsuno, A. Doi, M. Nagasaki, and S. Miyano (2000) Hybrid Petri net representation of gene regulatory network. In: Pac Symp Biocomput, pp 341–352
C.A. Petri (1962) PhD Thesis, Institut fûr Instrumentelle Mathematik, Bonn
M. Svadova (2001) Modelling Hybrid Dynamic Systems Using Hybrid Petri Nets. In: 13th Int. Conference on Process Control
Paul Greengard (2001) The neurobiology of slow synaptic transmission. Science; Vol. 294, pp 1024–1030
A.V. Hill (1910) The heat produced by contracture and muscular tone. Journal of Physiol. Lond.40, pp. 389–403
Mor Peleg, Iwei Yeh and Russ B. Altman (2002) Modeling biological processes using Workflow and Petri Net models. Bioinformatics Vol. 18 no.6, pp 825–837
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© 2005 Springer-Verlag Berlin Heidelberg
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Peng, S.C., Chang, HM., Hsu, D.F., Tang, C.Y. (2005). Modeling Signal Transduction of Neural System by Hybrid Petri Net Representation. In: Fleuren, H., den Hertog, D., Kort, P. (eds) Operations Research Proceedings 2004. Operations Research Proceedings, vol 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27679-3_34
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DOI: https://doi.org/10.1007/3-540-27679-3_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24274-1
Online ISBN: 978-3-540-27679-1
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