One of the hallmarks of cancer is a deregulated energy metabolism. A well-known characteristic is the Warburg effect describing an increased glucose uptake and lactate release of the cells, identified first 100 years ago by Otto Warburg. In recent years, more global changes in the metabolism of cancer cells have been described, in particular in biosynthetic pathways, e.g. the amino acid and nucleotide metabolism. Also, the impact of oncogenes, e.g. MYC family members, on metabolic processes has been studied in great detail.

A powerful tool to study metabolic changes in cancer cells are computational models. These often are either relatively small-scale ordinary differential equation models (ODEs) or genome-scale metabolic stoichiometric models (GEMs). While ODE models give detailed insights on the pathways kinetic behavior, they include a limited number of metabolites and often neglect the multitude of connections to neighboring pathways. GEMs on the other hand usually focus on the stoichiometry of the reactions and the steady state flux distribution, but aim to include all known metabolic processes.

In our work, we developed a detailed ODE model of the energy metabolism of a childhood cancer and analyzed the influence of the high-risk oncogene MYCN. Moreover, we aim to embed detailed kinetic models of the energy metabolism into large-scale GEMs. Therefore, we applied an algorithmic reduction of a GEM to derive stoichiometrically correct rates for the withdrawal and import of biosynthetic precursors of an ODE model during growth.