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In Silico Biology 10, 0004 (2010); ©2009, Bioinformation Systems e.V.  

On determining firing delay time of transitions for Petri net based signaling pathways by introducing stochastic decision rules

Yoshimasa Miwa1,#, Chen Li2,#, Qi-Wei Ge3, Hiroshi Matsuno1,* and Satoru Miyano2

1 Graduate School of Science and Engineering, Yamaguchi University, Yoshida 1677-1, Yamaguchi, 753-8512, Japan
2 Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
3 Faculty of Education, Yamaguchi University, Yoshida 1677-1, Yamaguchi, 753-8513 Japan

* Corresponding author

Edited by E. Wingender; received October 13, 2009; revised December 31, 2009; accepted January 02, 2010; published January 17, 2010


Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

Keywords: Petri net, interleukin-1 (IL-1) signaling pathway, firing delay time, stochastic decision rule, conflict resolution