analysis of stochastic reaction networks with markov reward models
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.
stochastic reaction networks markov reward models systems biology