Easton, John M. (2009)
Ph.D. thesis, University of Birmingham.
Since the completion of the Human Genome Project in 2003, it has become increasingly apparent that while genomics has a major role to play in the understanding of human biology, information from other disciplines is necessary to explain the web of interacting signals that allow our bodies to function on a day to day basis and respond to rapid changes in our local environment. One such field, that of metabolomics, focuses on the study of the set of low molecular weight compounds (metabolites) involved in metabolism. Metabolomic studies aim to quantify the concentrations of each of these compounds within a subject under particular conditions, resulting in either information on the physiological effects of a disease or environmental factor (such as a toxin) on the organism, or the identification of metabolites or groups of metabolites that serve as biochemical markers for a state or illness. Whilst metabolite concentrations alone can give great insight into a chosen state, additional information can be obtained by considering the ways in which metabolites interact with each other as parts of a larger system. One method of tackling this problem, metabolic networks, is gaining popularity within the community as it offers a complementary approach to the traditional biological method for studying metabolism, the metabolic pathway. Construction methods are varied; ranging from the mapping of experimental data onto pathway diagrams, through the use of correlation-based techniques, to the analysis of time-series data of metabolic fluxes. However, while many attempts have been made to capture and visualise the complex web of reactions within an organism, few have yet succeeded in showing how they can be used to help identify the metabolites that are most significantly involved in the differences between groups of biological samples. This thesis discusses ways in which graphs may be used to aid researchers in both the visualisation and interpretation of metabolomic datasets, and provide a platform for more automated analysis techniques. To that end, it first presents the background to the relevant topics, metabolomics and graph theory, before moving on to show how metabolic correlation networks can be used to identify and visualise differences in metabolism between groups of subjects. It then introduces Linked Metabolites, a software package that has been developed to help researchers explain differences in metabolism by highlighting relationships between metabolites within the metabolic pathways, and to compile those relationships into directed metabolic graphs suitable for analysis using metrics from graph theory. Finally, the thesis explains how the directed metabolic graphs produced by Linked Metabolites could potentially be used to integrate data gathered from the same sample using different experimental techniques, refining the areas of the underlying biochemistry needing further investigation.
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