title: MCMC estimation of Markov models for ion channels.
abstract: Ion channels are characterized by inherently stochastic behavior which can be represented by continuous-time Markov models (CTMM). Although methods for collecting data from single ion channels are available, translating a time series of open and closed channels to a CTMM remains a challenge. Bayesian statistics combined with Markov chain Monte Carlo (MCMC) sampling provide means for estimating the rate constants of a CTMM directly from single channel data. In this article, different approaches for the MCMC sampling of Markov models are combined. This method, new to our knowledge, detects overparameterizations and gives more accurate results than existing MCMC methods. It shows similar performance as QuB-MIL, which indicates that it also compares well with maximum likelihood estimators. Data collected from an inositol trisphosphate receptor is used to demonstrate how the best model for a given data set can be found in practice.
authors.affiliation: Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
authors.forename: Ivo
authors.initials: I
authors.lastname: Siekmann
authors.forename: Larry E
authors.initials: LE
authors.lastname: Wagner
authors.forename: David
authors.initials: D
authors.lastname: Yule
authors.forename: Colin
authors.initials: C
authors.lastname: Fox
authors.forename: David
authors.initials: D
authors.lastname: Bryant
authors.forename: Edmund J
authors.initials: EJ
authors.lastname: Crampin
authors.forename: James
authors.initials: J
authors.lastname: Sneyd
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D053496:Inositol 1,4,5-Trisphosphate Receptors / Q000378:metabolism; D015640:Ion Channel Gating; D007473:Ion Channels / Q000378:metabolism*; D016013:Likelihood Functions; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method
chemical_list: D053496:Inositol 1,4,5-Trisphosphate Receptors; D007473:Ion Channels