Probabilistic modelling
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Petri Net Theory and the Modeling of Systems
Petri Net Theory and the Modeling of Systems
Computational methods for stochastic biological systems
Computational methods for stochastic biological systems
Hybrid simulation of cellular behavior
Bioinformatics
Performance Analysis Using Stochastic Petri Nets
IEEE Transactions on Computers
The Petri net markup language: concepts, technology, and tools
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
Petri nets for modelling metabolic pathways: a survey
Natural Computing: an international journal
Petri net models for the semi-automatic construction of large scale biological networks
Natural Computing: an international journal
Modelling and analysing genetic networks: from boolean networks to petri nets
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
Stochastic simulation of the coagulation cascade: a petri net based approach
Euro-Par'12 Proceedings of the 18th international conference on Parallel processing workshops
Mining for variability in the coagulation pathway: a systems biology approach
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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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.