Stochastic simulation of multiple process calculi for biology

  • Authors:

  • Matthew R. Lakin;Loïc Paulevé;Andrew Phillips

  • Affiliations:

  • Microsoft Research, Cambridge, United Kingdom and Department of Computer Science, University of New Mexico, Albuquerque, NM, USA;LUNAM Université, École Centrale de Nantes, IRCCyN UMR CNRS 6597, 1 rue de la Noë, B.P. 92101, 44321 Nantes Cedex 3, France and LIX, École Polytechnique, 91128 Palaiseau Cedex, ...;Microsoft Research, Cambridge, United Kingdom

  • Venue:
  • Theoretical Computer Science
  • Year:
  • 2012

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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.