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.
Keywords: systems biology; stochastic reaction networks; Markov
reward models.
ACM Computing Classification: G.3. Markov processes.