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- W1898045352 abstract "Cellular response to a changing chemical environment is mediated by a complex system of interactionsinvolving molecules such as genes, proteins and metabolites. In particular, genetic and epigenetic variationensure that cellular response is often highly specific to individual cell types, or to different patientsin the clinical setting. Conceptually, cellular systems may be characterised as networks of interactingcomponents together with biochemical parameters specifying rates of reaction. Taken together, the networkand parameters form a predictive model of cellular dynamics which may be used to simulate theeffect of hypothetical drug regimens.In practice, however, both network topology and reaction rates remain partially or entirely unknown,depending on individual genetic variation and environmental conditions. Prediction under parameteruncertainty is a classical statistical problem. Yet, doubly uncertain prediction, where both parametersand the underlying network topology are unknown, leads to highly non-trivial probability distributionswhich currently require gross simplifying assumptions to analyse. Recent advances in molecular assaytechnology now permit high-throughput data-driven studies of cellular dynamics. This thesis sought todevelop novel statistical methods in this context, focussing primarily on the problems of (i) elucidatingbiochemical network topology from assay data and (ii) prediction of dynamical response to therapy whenboth network and parameters are uncertain." @default.
- W1898045352 created "2016-06-24" @default.
- W1898045352 creator A5075711744 @default.
- W1898045352 date "2013-08-01" @default.
- W1898045352 modified "2023-09-26" @default.
- W1898045352 title "Bayesian inference for protein signalling networks" @default.
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