We consider discrete-space continuous-time Markov models of reaction networks and provide sufficient conditions for positive recurrence . The provided analytical results depend solely on the underlying structure of the reaction network and not on the specific choice of model parameters. Our main results apply to binary systems and our main analytical tool is the "tier structure" previously utilized successfully in the study of deterministic models of reaction networks.
We consider discrete-space continuous-time Markov models of reaction networks and provide sufficient conditions for the following stability condition to hold: each state in a closed, irreducible component of the state space is positive recurrent; moreover the time required for a trajectory to enter such a component has finite expectation . The provided analytical results depend solely on the underlying structure of the reaction network and not on the specific choice of model parameters. Our main results apply to binary systems and our main analytical tool is the "tier structure" previously utilized successfully in the study of deterministic models of reaction networks.
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positive recurrence
the following stability condition to hold: each state in a closed, irreducible component of the state space is positive recurrent; moreover the time required for a trajectory to enter such a component has finite expectation