Thesis / Dissertation

Global a priori identifiability of models of flow-cell optical biosensor experiments

J Whyte, Anthony Guttmann (ed.), Peter Taylor (ed.)

Published : 2016

Abstract

Ideally, a parametric model for a biological system enables prediction of system behaviour for conditions where we lack observations. This necessitates first estimating parameters from some limited data series subject to random error, that is, solving an 'inverse problem'. A solution is some parameter vector that optimizes an objective function. For example, a solution may minimize a sum of squared errors. Multiple (equally valid) solutions may result in unresolvable uncertainty over which is the actual parameter vector. This is problematic as predictions for a system's observable features — and the unobservable 'state variables' influencing these — may vary drastically with the parameter ve..

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