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Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect

Overview of attention for article published in PLoS Computational Biology, March 2011
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Title
Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect
Published in
PLoS Computational Biology, March 2011
DOI 10.1371/journal.pcbi.1002018
Pubmed ID
Authors

Tomer Shlomi, Tomer Benyamini, Eyal Gottlieb, Roded Sharan, Eytan Ruppin

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 397 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 3%
Germany 3 <1%
United Kingdom 3 <1%
Australia 2 <1%
Iran, Islamic Republic of 2 <1%
Chile 1 <1%
Brazil 1 <1%
Sweden 1 <1%
Switzerland 1 <1%
Other 14 4%
Unknown 356 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 102 26%
Researcher 99 25%
Student > Master 43 11%
Student > Bachelor 25 6%
Professor > Associate Professor 21 5%
Other 65 16%
Unknown 42 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 167 42%
Biochemistry, Genetics and Molecular Biology 59 15%
Engineering 31 8%
Medicine and Dentistry 22 6%
Computer Science 19 5%
Other 37 9%
Unknown 62 16%