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


Other title
ion channels
Type of item
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 of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
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.
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