Contributed Talk

Coarse-grained computational models of cancer metabolism and cellular growth

Humboldt-University of Berlin, Institute for Theoretical Biology, Philippstr. 13, Haus 20, 10115 Berlin, Germany

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Cancer is (also) a metabolic disease. The Warburg effect, an increase in the rate of glucose uptake and production of lactate, is among the best-documented hallmarks of cancer. In general, cancer cells rewire their metabolism to promote growth and proliferation, thereby giving rise to a different metabolic profile compared to healthy cells. This contribution will describe recent efforts to understand and computationally model the transition from a healthy cell to cancer. In particular, genome-scale reconstructions of human metabolism allow us to construct cell-type and condition-specific stoichiometric models. While such comprehensive genome-scale metabolic reconstructions offer unprecedented possibilities to study human metabolism, large-scale stoichiometric models also have several disadvantages and do not capture the dynamic nature of metabolism. An alternative approach is to construct dynamic coarse-grained models of metabolism and growth that aim to incorporate correct overall stoichiometries and energy requirements of cellular metabolism, but otherwise reduce reactions to a small number of overall reactions. I will present such a coarse-grained model of human metabolism and growth, as well as an analysis of its kinetic properties. It is shown that the model can recapitulate the transition from a healthy non-growing phenotype to cancer metabolism. Our ultimate aim is to obtain a quantitative understanding of disease genesis and progression in terms of the underlying cellular networks of biochemical interactions.