On the Use of Stochastic Petri Nets in the Analysis of Signal Transduction Pathways for Angiogenesis Process

  • Authors:

  • Lucia Napione;Daniele Manini;Francesca Cordero;András Horváth;Andrea Picco;Massimiliano Pierro;Simona Pavan;Matteo Sereno;Andrea Veglio;Federico Bussolino;Gianfranco Balbo

  • Affiliations:

  • Institute for Cancer Research and Treatment, Candiolo, Italy and Department of Oncological Sciences, University of Torino, Torino, Italy;Department of Computer Science, University of Torino, Torino, Italy;Department of Computer Science, University of Torino, Torino, Italy and Department of Clinical and Biological Sciences, University of Torino, Torino, Italy;Department of Computer Science, University of Torino, Torino, Italy;Institute for Cancer Research and Treatment, Candiolo, Italy and Department of Oncological Sciences, University of Torino, Torino, Italy;Department of Computer Science, University of Torino, Torino, Italy;Institute for Cancer Research and Treatment, Candiolo, Italy and Department of Oncological Sciences, University of Torino, Torino, Italy;Department of Computer Science, University of Torino, Torino, Italy;Institute for Cancer Research and Treatment, Candiolo, Italy and Department of Oncological Sciences, University of Torino, Torino, Italy;Institute for Cancer Research and Treatment, Candiolo, Italy and Department of Oncological Sciences, University of Torino, Torino, Italy;Department of Computer Science, University of Torino, Torino, Italy

  • Venue:
  • CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
  • Year:
  • 2009

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Abstract

In this paper we consider the modeling of a selected portion of signal transduction events involved in the angiogenesis process. The detailed model of this process contains a large number of parameters and the data available from wet-lab experiments are not sufficient to obtain reliable estimates for all of them. To overcome this problem, we suggest ways to simplify the detailed representation that result in models with a smaller number of parameters still capturing the overall behaviour of the detailed one. Starting from a detailed stochastic Petri net (SPN) model that accounts for all the reactions of the signal transduction cascade, using structural properties combined with the knowledge of the biological phenomena, we propose a set of model reductions.