title: Bayesian approaches for mechanistic ion channel modeling.
abstract: We consider the Bayesian analysis of mechanistic models describing the dynamic behavior of ligand-gated ion channels. The opening of the transmembrane pore in an ion channel is brought about by conformational changes in the protein, which results in a flow of ions through the pore. Remarkably, given the diameter of the pore, the flow of ions from a small number of channels or indeed from a single ion channel molecule can be recorded experimentally. This produces a large time-series of high-resolution experimental data, which can be used to investigate the gating process of these channels. We give a brief overview of the achievements and limitations of alternative maximum-likelihood approaches to this type of modeling, before investigating the statistical issues associated with analyzing stochastic model reaction mechanisms from a Bayesian perspective. Finally, we compare a number of Markov chain Monte Carlo algorithms that may be used to tackle this challenging inference problem.
authors.affiliation: Department of Computing Science, University of Glasgow, Glasgow, Scotland.
authors.forename: Ben
authors.initials: B
authors.lastname: Calderhead
authors.forename: Michael
authors.initials: M
authors.lastname: Epstein
authors.forename: Lucia
authors.initials: L
authors.lastname: Sivilotti
authors.forename: Mark
authors.initials: M
authors.lastname: Girolami
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D015640:Ion Channel Gating; D007473:Ion Channels / Q000737:chemistry / Q000378:metabolism*; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D049490:Systems Biology
chemical_list: D007473:Ion Channels