Metabolic flux prediction in cancer cells with altered substrate uptake.

Proliferating cells, such as cancer cells, are known to have an unusual metabolism, characterized by an increased rate of glycolysis and amino acid metabolism. Our understanding of this phenomenon is limited but could potentially be used in order to develop new therapies. Computational modelling techniques, such as flux balance analysis (FBA), have been used to predict fluxes in various cell types, but remain of limited use to explain the unusual metabolic shifts and altered substrate uptake in human cancer cells. We implemented a new flux prediction method based on elementary modes (EMs) and structural flux (StruF) analysis and tested them against experimentally measured flux data obtained from (13)C-labelling in a cancer cell line. We assessed the quality of predictions using different objective functions along with different techniques in normalizing a metabolic network with more than one substrate input. Results show a good correlation between predicted and experimental values and indicate that the choice of cellular objective critically affects the quality of predictions. In particular, lactate gives an excellent correlation and correctly predicts the high flux through glycolysis, matching the observed characteristics of cancer cells. In contrast with FBA, which requires a priori definition of all uptake rates, often hard to measure, atomic StruFs (aStruFs) are able to predict uptake rates of multiple substrates.