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Permanent link (DOI): https://doi.org/10.7939/R33N20N9Z

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Optimization of Ion channel recordings through analysis of multiple fittings Open Access

Descriptions

Other title
Subject/Keyword
HMM
overfitting
ion channels
optimization
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Luchko, Aaron C
Supervisor and department
Jones, Kelvin (Physical Education and Recreation)
Wong, Ken (Computing Science)
Examining committee member and department
Gallin, Warren (Biology)
Department
Department of Computing Science
Specialization

Date accepted
2014-08-13T09:07:00Z
Graduation date
2014-11
Degree
Master of Science
Degree level
Master's
Abstract
We develop an approach for optimizing Hidden Markov model representations of voltage-gated ion channels that addresses the issues of topology determination and poorly performing optimization algorithms. Developing accurate models of neurological processes is a major goal of computational neuroscience, but creating accurate models of voltage-gated ion channels is a difficult task. Noisy data, a large range of potential topologies, and large numbers of parameters make machine optimization very difficult and topology comparison techniques unreliable. We attempt to address the unreliability of the optimization process through multiple fittings. We then analyze the sets of fitted models with a new metric designed to measure consistency in the behaviour of the hidden states. When combined with the LogLikelihood this indicates whether the model has the complexity necessary to fit the data. We then design a protocol based around the creation of multiple fitted models that utilizes this metric both as a guide for further fittings and a way to identify a selection of suitable models and topologies. We apply the metric to five sets of simulated data and two pairs of live recordings of voltage-gated K+ channels. On the simulated data the described protocol generated a range of topologies that successfully captured the correct topology in all but one of the simulated trials where it underestimated the topology required. Applied to the live data the procedure performed well on one channel type, for the other results were impacted by the difficulty of the optimization problem. In general the procedure and metric performed well but were limited by the ability of the optimizer to deliver a range of high quality solutions.
Language
English
DOI
doi:10.7939/R33N20N9Z
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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