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Bayesian approaches for mechanistic ion channel modeling

Calderhead, B; Epstein, M; Sivilotti, L; Girolami, M; (2013) Bayesian approaches for mechanistic ion channel modeling. Methods in Molecular Biology , 1021 pp. 247-272. 10.1007/978-1-62703-450-0-13.

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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. © 2013 Springer Science+Business Media, LLC.

Type: Article
Title: Bayesian approaches for mechanistic ion channel modeling
DOI: 10.1007/978-1-62703-450-0-13
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Neuro, Physiology and Pharmacology
URI: http://discovery.ucl.ac.uk/id/eprint/10031974
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