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Spontaneous Reaction Silencing in Metabolic Optimization

Overview of attention for article published in PLoS Computational Biology, December 2008
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Title
Spontaneous Reaction Silencing in Metabolic Optimization
Published in
PLoS Computational Biology, December 2008
DOI 10.1371/journal.pcbi.1000236
Pubmed ID
Authors

Takashi Nishikawa, Natali Gulbahce, Adilson E. Motter

Abstract

Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances. Yet, the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood. Here we predict computationally and analytically that any organism evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical nonoptimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all. We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways, shedding new light on microbial evolution, robustness, and versatility for the execution of specific biochemical tasks. In particular, the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Germany 2 2%
Switzerland 2 2%
Iran, Islamic Republic of 2 2%
Gambia 1 1%
India 1 1%
Portugal 1 1%
Spain 1 1%
Australia 1 1%
Other 0 0%
Unknown 68 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 33%
Student > Ph. D. Student 25 30%
Professor > Associate Professor 9 11%
Professor 7 8%
Student > Master 4 5%
Other 6 7%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 55%
Physics and Astronomy 8 10%
Biochemistry, Genetics and Molecular Biology 7 8%
Computer Science 4 5%
Environmental Science 3 4%
Other 9 11%
Unknown 6 7%