Abstract
The metabolic behavior of cancer cells is adapted to meet their proliferative needs, 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. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets.
Highlights
Metabolic regulation plays a key role in fulfilling the metabolic demands of proliferative vs. quiescent tissue
GROWTH RATE AND METABOLITE MEASUREMENTS The experimental growth rate for the colo205 cells was used as biomass production rate in the flux balance analysis (FBA) model (Figure 2A)
Notable trends include the high rates of glutamine and glucose uptake, consistent with the idea that these are the main source of nutrient uptake in cancer cells (DeBerardinis et al, 2008)
Summary
Metabolic regulation plays a key role in fulfilling the metabolic demands of proliferative vs. quiescent tissue. The distinguished metabolism of cancer cells compared to differentiated tissue was first observed by Otto Warburg in the 1920s where he noted high rates of glucose consumption and lactate secretion, regardless of oxygen availability. This metabolic behavior in cancer cells is termed aerobic glycolysis or the “Warburg effect.” (Vander Heiden et al, 2009). Pyruvate subsequently enters the TCA cycle in the mitochondria and is oxidized to produce carbon dioxide This oxidation in the TCA cycle generates NADH, which fuels oxidative phosphorylation for the maximal production of ATP with minimal lactate production. Cancer cells display “aerobic glycolysis” and convert most of the glucose into lactate regardless of the presence of oxygen (Vander Heiden et al, 2009)
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