A kinetically-constrained FBA-model of the synthesis of aromatic amino acid-derived products in Saccharomyces cerevisiae

During the last decades the research interest for creating a more sustainable and environmentallyfriendly society and industry has increased dramatically. The employment of icroorganisms to create valuable compounds such as fuels and chemicals from renewable resources has gained high popularity. With directed modifications in the metabolism of these microorganisms, metabolic engineering seeks to optimize the properties of these organisms for an efficient bioprocess to produce valuable compounds. One characteristic of metabolic engineering is the extensive use of computational models to predict the behavior of the metabolism and thereby identifying suitable targets for genetic interventions in an in silico to in vivo progress. Flux Balance Analysis (FBA) is a popular analysis method for metabolic models, as it only requires the underlying stoichiometric network of the metabolism modelled. With FBA the optimal flux distribution, given a certain objective to be maximized, can be calculated with linear programming algorithms. As FBA only takes the stoichiometry and reaction directionality into account, further physiological constraints have to be included in the framework to increase the predictive strength of the simulations. In this thesis, an expanded form of FBA, that includes kinetic enzyme parameters as additional constraints on the system, was used to analyze a metabolic model of the popular industrially-used organisms Saccharomyces cerevisiae, that included, additionally to the native metabolism, also recombinant enzymatic steps to form the plant secondary metabolites resveratrol and naringenin from the aromatic amino acids phenylalanine and tyrosine. Resveratrol and naringenin have been found to be beneficial to human health by offering anti-inflammatory, anti-carcinogenesis and anti-oxidant properties. The kinetically-constrained model was successfully used to estimate the impact of introducing recombinant pathways on the protein pool in the cell as well as to identify the enzymatic steps offering the highest control over the flux towards these products. This might be used in metabolic engineering to estimate the efficiency of metabolic pathways in the context of the whole metabolism form a protein cost point of view