Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.668440
Title: Parameterising and evaluating Markov models for ion-channels
Author: Epstein, M. J.
ISNI:       0000 0004 5367 0796
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2015
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Abstract:
Ligand gated ion-channels are proteins that are embedded in the membranes of cells. They play many crucial physiological roles, enabling fast cell-to-cell communication be- tween neuronal cells and communication between the nervous and musculoskeletal sys- tems. A key feature of their physiology is their ability to form a pore, through which a small quantal current in the form of ions can ow. Such channels are statistically modelled as Markov processes. However, given the limitations of experimental data, establishing model parameter identi�ability and robust model comparison remain im- portant statistical issues. This Thesis considers the use of Likelihood and Bayesian methods to investigate the parameterisation and selection of mechanistic Markov models for ion-channel gating. The biological and statistical background is placed in context, particularly with regard to statistical issues of limited time resolution of electrophysiological recordings which signi�cantly complicates the model likelihood. A canonical ion-channel model for the Acetylcholine receptor is described, which is used to assess the use of pro�le likelihoods to answer questions concerning model parameter identi�ability. MCMC techniques are then introduced to sample from model posterior distributions in order to examine candidate models within the Bayesian paradigm. Even simple mod- els exhibit complex posterior distributions. This motivates a thorough assessment of sophisticated MCMC samplers to perform Bayesian inference in such models. A prin- cipled method using preconditioned or adaptive MCMC algorithms is found to provide an e�ective sampling strategy. Model parameterisation and predictive uncertainty in model posterior outputs is then assessed using both real and synthetic data. Model discrimination based on visual inspection of posterior predictive output is not always conclusive. A parallel tempering sampling strategy is successfully implemented to estimate Bayes Factors for candidate models. This quantitative technique can dis- criminate between competing models that otherwise produce visually similar predictive output for the gating dynamics of the Acetylcholine receptor.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.668440  DOI: Not available
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