Analysis of stochastic reaction networks with Markov reward models

  • Authors:

  • Alessio Angius;András Horváth

  • Affiliations:

  • University of Turin, Via Pessinetto, Turin, Italy;University of Turin, Via Pessinetto, Turin, Italy

  • Venue:
  • Proceedings of the 9th International Conference on Computational Methods in Systems Biology
  • Year:
  • 2011

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Abstract

This paper considers Markov chains describing stochastic reaction networks. These Markov chains often have a huge state space which make their analysis unfeasible. We show that there exist cases when the original Markov chain can be transformed into a Markov reward model with a smaller state space and whose analysis gives information on the moments of the quantity of the involved species. We derive the necessary mathematics and provide numerical examples to illustrate the approach.