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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


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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 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 system's 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 E.coli. © 2006.

Publication metadata

Author(s): Shaw O, Steggles J, Wipat A

Editor(s): N. Thomas

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2nd International Workshop on the Practical Application of Stochastic Modeling (PASM 2005)

Year of Conference: 2005

Pages: 111-129

ISSN: 1571-0661

Publisher: Electronic Notes in Theoretical Computer Science: Elsevier


DOI: 10.1016/j.entcs.2006.03.015