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

  • Lucia Napione
  • Daniele Manini
  • Francesca Cordero
  • András Horváth
  • Andrea Picco
  • Massimiliano De Pierro
  • Simona Pavan
  • Matteo Sereno
  • Andrea Veglio
  • Federico Bussolino
  • Gianfranco Balbo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5688)

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.

Keywords

Vascular Endothelial Growth Factor Signal Transduction Pathway Transition Rate Detailed Model Ordinary Differential Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lucia Napione
    • 2
    • 4
  • Daniele Manini
    • 1
  • Francesca Cordero
    • 1
    • 3
  • András Horváth
    • 1
  • Andrea Picco
    • 2
    • 4
  • Massimiliano De Pierro
    • 1
  • Simona Pavan
    • 2
    • 4
  • Matteo Sereno
    • 1
  • Andrea Veglio
    • 2
    • 4
  • Federico Bussolino
    • 2
    • 4
  • Gianfranco Balbo
    • 1
  1. 1.Department of Computer ScienceUniversity of TorinoTorinoItaly
  2. 2.Institute for Cancer Research and TreatmentCandioloItaly
  3. 3.Department of Clinical and Biological SciencesUniversity of TorinoTorinoItaly
  4. 4.Department of Oncological SciencesUniversity of TorinoTorinoItaly

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