Stochastic Simulation of Process Calculi for Biology

Andrew Phillips
(Microsoft Research)
Matthew Lakin
(Microsoft Research)
Loïc Paulevé
(Ecole Centrale de Nantes)

Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. 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. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie's Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework.

In Gabriel Ciobanu and Maciej Koutny: Proceedings Fourth Workshop on Membrane Computing and Biologically Inspired Process Calculi 2010 (MeCBIC 2010), Jena, Germany, 23 August 2010, Electronic Proceedings in Theoretical Computer Science 40, pp. 1–5.
Published: 30th October 2010.

ArXived at: https://dx.doi.org/10.4204/EPTCS.40.1 bibtex PDF

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