Oates, Chris J. (2013) Bayesian inference for protein signalling networks. PhD thesis, University of Warwick.

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Abstract

Cellular response to a changing chemical environment is mediated by a complex system of interactions
involving molecules such as genes, proteins and metabolites. In particular, genetic and epigenetic variation
ensure that cellular response is often highly specific to individual cell types, or to different patients
in the clinical setting. Conceptually, cellular systems may be characterised as networks of interacting
components together with biochemical parameters specifying rates of reaction. Taken together, the network
and parameters form a predictive model of cellular dynamics which may be used to simulate the
effect 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 parameter
uncertainty is a classical statistical problem. Yet, doubly uncertain prediction, where both parameters
and the underlying network topology are unknown, leads to highly non-trivial probability distributions
which currently require gross simplifying assumptions to analyse. Recent advances in molecular assay
technology now permit high-throughput data-driven studies of cellular dynamics. This thesis sought to
develop novel statistical methods in this context, focussing primarily on the problems of (i) elucidating
biochemical network topology from assay data and (ii) prediction of dynamical response to therapy when
both network and parameters are uncertain.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics Q Science > QP Physiology
Library of Congress Subject Headings (LCSH):	Protein-protein interactions, Cellular signal transduction, Bayesian statistical decision theory
Official Date:	August 2013
Dates:	Date Event August 2013 Submitted
Institution:	University of Warwick
Theses Department:	Centre for Complexity Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	[Supervisor not provided].
Sponsors:	Engineering and Physical Sciences Research Council (EPSRC)
Extent:	112 pages : illustrations, charts.
Language:	eng

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