Towards a full genome-scale model of yeast metabolism
[Thesis]. Manchester, UK: The University of Manchester; 2011.
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Gaining a quantitative understanding of metabolic behaviour has long been a major scientific goal. Beginning with crude mass balance experiments and progressing through enzyme kinetics, single-pathway models and collaborative efforts such as a community- based yeast reconstruction and onwards to the digital human. The primary goal of this research was to generate a large-scale kinetic metabolic model of yeast metabolism. As a community our ability to produce large-scale dynamic metabolic models has typically been limited by the time and cost involved in obtaining exact measurements of all relevant kinetic parameters. Attempts have been made to bring about a greater understanding by using computational approaches such as flux balance analysis, and also laboratory approaches such as metabolic profiling. Unfortunately these approaches alone do not go far enough to allow for a rich understanding of the metabolic behaviour.Methods were developed that allowed known data such as fluxes, equilibrium constants and metabolite concentrations to be used in first-approximation strategies. These made possible the construction of a thermodynamically consistent model that was reflective of the organism and growth conditions under which the known data were measured. Efforts were made to improve the strategy by developing already known dynamic flux measurement techniques so they were more reflective of the type of data required for constructing the metabolic model.The model constructed, using data from a specific yeast strain in a continuous culture environment, and included 284 reactions. The model showed a reasonable reproduction of system behaviour after perturbations of extracellular glucose above and below the operating conditions, after identification and substitution of just two exact rate laws of reactions that showed high control over the system.The methods developed require little knowledge beyond the stoichiometric matrix in the first instance, and as such, are applicable to any organism that has a reasonably comprehensive network reconstruction available.