CONTINUOUS-TIME MARKOV-CHAINS IN A RANDOM ENVIRONMENT, WITH APPLICATIONS TO ION-CHANNEL MODELING

We study a bivariate stochastic process {X(t)}={(X(E)(t))}, Z(t))}, where {X(E)(t)} is a continuous-time Markov chain describing the environment and {Z(t)} is the process of primary interest. In the context which motivated this study, {Z(t)} models the gating behaviour of a single ion channel. It is assumed that given {X,(t)}, the channel process {Z(t)} is a continuous-time Markov chain with infinitesimal generator at time t dependent on X(E)(t), and that the environment process {X(E)(t)} is not dependent on {Z(t)}. We derive necessary and sufficient conditions for {X(t)} to be time reversible, showing that then its equilibrium distribution has a product form which reflects independence of the state of the environment and the state of the channel. In the special case when the environment controls the speed of the channel process, we derive transition probabilities and sojourn time distributions for {Z(t)} by exploiting connections with Markov reward processes. Some of these results are extended to a stationary environment. Applications to problems arising in modelling multiple ion channel systems are discussed. In particular, we present ways in which a multichannel model in a random environment does and does not exhibit behaviour identical to a corresponding model based on independent and identically distributed channels.