Stochastic simulation of multiple process calculi for biology

Matthew Lakin 1, 2 Loïc Paulevé 3 Andrew Phillips 1
3 AMIB - Algorithms and Models for Integrative Biology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
Abstract : Numerous programming languages based on process calculi have been developed for biological modelling, many of which can generate potentially unbounded numbers of molecular species and reactions. As a result, such languages cannot rely on standard reaction-based simulation methods, and are generally implemented using custom stochastic simulation algorithms. As an alternative, this paper proposes a generic abstract machine that can be instantiated to simulate a range of process calculi using a range of simulation methods. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. We instantiate the generic abstract machine with two Markovian simulation methods and provide encodings for four process calculi: the agent-based pi-calculus, the compartment-based bioambient calculus, the rule-based kappa calculus and the domain-specific DNA strand displacement calculus. We present a generic method for proving that the encoding of an arbitrary process calculus into the abstract machine is correct, and we use this method to prove the correctness of all four calculus encodings. Finally, we demonstrate how the generic abstract machine can be used to simulate heterogeneous models in which discrete communicating sub-models are written using different domain-specific languages and then simulated together. Our approach forms the basis of a multi-language environment for the simulation of heterogeneous biological models.
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Theoretical Computer Science, Elsevier, 2012, 431, pp.181-206. 〈10.1016/j.tcs.2011.12.057〉
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Contributeur : Loïc Paulevé <>
Soumis le : lundi 14 octobre 2013 - 18:05:34
Dernière modification le : jeudi 7 février 2019 - 16:05:32

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Matthew Lakin, Loïc Paulevé, Andrew Phillips. Stochastic simulation of multiple process calculi for biology. Theoretical Computer Science, Elsevier, 2012, 431, pp.181-206. 〈10.1016/j.tcs.2011.12.057〉. 〈hal-00872969〉

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