Combining Model Reductions

  • Authors:

  • Ferdinanda Camporesi;Jérôme Feret;Heinz Koeppl;Tatjana Petrov

  • Affiliations:

  • Dipartimento di Scienze dell'Informazione, Universití di Bologna, Bologna, Italy and Laboratoire d'informatique de l'ícole normale supérieure, (INRIA/íNS/CNRS), Paris, France;Laboratoire d'informatique de l'ícole normale supérieure, (INRIA/ENS/CNRS), Paris, France;School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland;School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland

  • Venue:
  • Electronic Notes in Theoretical Computer Science (ENTCS)
  • Year:
  • 2010

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Abstract

Molecular biological models usually suffer from a large combinatorial explosion. Indeed, proteins form complexes and modify each others, which leads to the formation of a huge number of distinct chemical species (i.e. non-isomorphic connected components of proteins). Thus we cannot generate explicitly the quantitative semantics of these models, and even less compute their properties. Model reduction aims at reducing this complexity by providing another grain of observation. In this paper, we propose two unifying frameworks for combining model reductions: we propose a symmetric product operator for combining model reductions for stochastic semantics and we show how to abstract further existing reduced differential systems by the means of linear projections. We apply both frameworks so as to abstract further existing reduced quantitative semantics of the models that are written in Kappa, by taking into account symmetries among binding sites in proteins.