Using a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, this model shows that the Warburg effect (a classical hallmark of cancer metabolism) is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate.

This model is described in the article:

Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect.

Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E

PLoS Computational Biology. 2011; 7(3):e1002018

Abstract:

The Warburg effect - a classical hallmark of cancer metabolism - is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids.