Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/132115
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dc.titleAUTOMATING ION CHANNEL MODEL DEVELOPMENT
dc.contributor.authorQUEK YU XUAN JANE
dc.date.accessioned2016-11-30T18:00:20Z
dc.date.available2016-11-30T18:00:20Z
dc.date.issued2016-07-29
dc.identifier.citationQUEK YU XUAN JANE (2016-07-29). AUTOMATING ION CHANNEL MODEL DEVELOPMENT. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/132115
dc.description.abstractAs ion channels enjoy ubiquitous importance in living cells, the characterisation of their electrical activity plays a salient role in virtually all physiologically-related fields. Automated patch-clamp electrophysiological platforms have emerged as high-throughput frontrunners for measuring ion channel activity. However, their measurements remain underutilised in multiscale modelling due to 1) large volumes of generated data 2) the lack of a suitable model capable of integrating both structural and functional information, whilst maintaining computational effi ciency and statistical soundness. This thesis investigates the feasibility of developing such a model using the Manifest Interconductance Rank form and Eyring Rate Theory. Unlike previous models, this model is designed to continually evolve, by automatically 'learning' from the ever-increasing data generated from high-throughput technologies. Through simulation and optimisation experiments, crucial modelling constraints have been identified. A novel goodness-of-fit criterion necessary for objective assessment of the evolving non-linear models has also been created.
dc.language.isoen
dc.subjection channel model, automate, Manifest Interconductance Rank canonical form, goodness-of-fit, heuristic, Eyring rate theory
dc.typeThesis
dc.contributor.departmentBIOMEDICAL ENGINEERING
dc.contributor.supervisorBUIST, MARTIN LINDSAY
dc.contributor.supervisorALBERTO CORRIAS
dc.description.degreeMaster's
dc.description.degreeconferredMASTER OF ENGINEERING
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Master's Theses (Open)

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