title: Graphs, random sums, and sojourn time distributions, with application to ion-channel modeling.
abstract: This paper considers the distribution of a sojourn time in a class of states of a stochastic process having finite discrete state space where sojourn times in any individual state are independent and identically distributed, and transitions between states follow a Markov chain. The state space and possible transitions of the process are represented by a graph. Class sojourn time distributions are derived by modifying this graph using 'composition' of states, defining a new Markov chain on the modified graph, and expressing the sojourn time in a composition state as a random sum. Appropriate compositions are chosen according to the possible "cores" of sojourns in the particular class, where a core describes the structure of a sojourn in terms of a single state or a chain in the original graph. Graph methods provide an algorithmic basis for the derivation, which can be simplified by using symmetry results. Models of ion-channel kinetics are used throughout for illustration; class sojourn time distributions are important in such models because individual states are often indistinguishable experimentally. Markov processes are the special case where sojourn times in individual states are exponentially distributed. In this case kinetic parameter estimation based on the observed class sojourn time distribution is briefly discussed; explicit estimating equations applicable to sequential models of nicotinic receptor kinetics are given.
authors.affiliation: Department of Anaesthesia, Sir Charles Gairdner Hospital, Nedlands, Western Australia.
authors.forename: R O
authors.initials: RO
authors.lastname: Edeson
authors.forename: G F
authors.initials: GF
authors.lastname: Yeo
authors.forename: R K
authors.initials: RK
authors.lastname: Milne
authors.forename: B W
authors.initials: BW
authors.lastname: Madsen
mesh_terms: D007473:Ion Channels / Q000378:metabolism*; D007700:Kinetics; D008390:Markov Chains; D008433:Mathematics; D008954:Models, Biological; D013269:Stochastic Processes
chemical_list: D007473:Ion Channels