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Automatic Parameterisation of Stochastic Petri Net Models of Biological Networks

Lookup NU author(s): Oliver Shaw, Dr Jason Steggles, Professor Anil Wipat



Stochastic simulations are able to capture the fine grain behaviour and randomness of outcome of biological networks not captured by deterministic techniques. As such they are becoming an increasingly important tool in the biological community. However, current efforts in the stochastic simulation of biological networks are hampered by two main problems: firstly the lack of complete knowledge of kinetic parameters; and secondly the computational cost of the simulations. In this paper we investigate these problems using the framework of stochastic Petri nets. We present a new stochastic Petri net simulation tool {\it NASTY} which allows large numbers of stochastic simulations to be carried out in parallel. We then begin to address the important problem of incomplete knowledge of kinetic parameters by developing a distributed genetic algorithm, based on NASTY's simulation engine, to parameterise stochastic networks. Our algorithm is able to successfully estimate kinetic parameters to replicate a systems behaviour and we illustrate this by presenting a case study in which the kinetic parameters are derived for a stochastic model of the stress response pathway in the bacterium \emph{E.coli}.

Publication metadata

Author(s): Shaw OJ, Steggles LJ, Wipat A

Publication type: Report

Publication status: Published

Series Title: School of Computing Science Technical Report Series

Year: 2005

Pages: 21

Print publication date: 01/05/2005

Source Publication Date: May 2005

Report Number: 909

Institution: School of Computing Science, University of Newcastle upon Tyne

Place Published: Newcastle upon Tyne