Predicting metabolic strategies in Saccharomyces cerevisiae with a kinetically constrained FBA model

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Examensarbete för masterexamen
Master Thesis
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2014
Författare
Nilsson, Avlant
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Metabolism is central to all life. It provides the energy and the building blocks from which the cells are constructed and maintained. Synthetic biologists often make genetic alterations to the enzymes involved in metabolism to improve product yields. Drastic changes in metabolism are linked to several diseases, e.g. cancer. It is therefore desirable to understand and quantitatively predict cell metabolism. Flux balance analysis (FBA) is a successful mathematical approach for predicting the metabolic activity of a cell. It makes use of the stoichiometry of the biochemical reactions and the rates of nutrient uptake. These relations are used to generate self consistent sets of metabolic fluxes, i.e. rates of metabolic conversion over the reactions. Amongst these it is common to select the set that has the highest growth rate as the predicted set. This has been shown to agree well with experimental data. One problem with the standard FBA approach is that it does not constrain the flux levels. In the living cell fluxes are constrained by the fact that they are performed by a finite amount of enzymes. The enzyme levels are limited by the amount of energy available for enzyme production and a limited space for enzymes to occupy. It has been shown that taking such limits in to account can improve the prediction powers of FBA. In this master thesis a modified version of FBA has been developed that uses the fluxes and enzyme kinetic parameters to estimates the weight of the participating enzymes. The total protein weight is constrained to experimentally observed levels. This allows prediction of the maximum growth rate for different substrates and shifts in metabolic strategy to fermento respiration at high growth rates. This might become of use to metabolic engineers in predicting if a potential pathway might decrease cell fitness.
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Livsvetenskaper, Annan fysik, Biologiska vetenskaper, Bioinformatik och systembiologi, Life Science, Other Physics Topics, Biological Sciences, Bioinformatics and Systems Biology
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