| Summary: | Metabolism in cancer cells is adapted to meet the proliferative needs of these cells, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. A metabolic network consisting of 58 reactions is considered which accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation. Experimentally measured metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinate-CoA ligase (SUCOAS1m) to display a significant reduction in growth rate when repressed relative to currently known drug targets. Furthermore, the synergetic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) showed a three fold decrease in growth rate compared to the repression of single enzyme targets.
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