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Phenomenological Model for Predicting the Catabolic Potential of an Arbitrary Nutrient

Overview of attention for article published in PLoS Computational Biology, November 2012
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Title
Phenomenological Model for Predicting the Catabolic Potential of an Arbitrary Nutrient
Published in
PLoS Computational Biology, November 2012
DOI 10.1371/journal.pcbi.1002762
Pubmed ID
Authors

Samuel M. D. Seaver, Marta Sales-Pardo, Roger Guimerà, Luís A. Nunes Amaral

Abstract

The ability of microbial species to consume compounds found in the environment to generate commercially-valuable products has long been exploited by humanity. The untapped, staggering diversity of microbial organisms offers a wealth of potential resources for tackling medical, environmental, and energy challenges. Understanding microbial metabolism will be crucial to many of these potential applications. Thermodynamically-feasible metabolic reconstructions can be used, under some conditions, to predict the growth rate of certain microbes using constraint-based methods. While these reconstructions are powerful, they are still cumbersome to build and, because of the complexity of metabolic networks, it is hard for researchers to gain from these reconstructions an understanding of why a certain nutrient yields a given growth rate for a given microbe. Here, we present a simple model of biomass production that accurately reproduces the predictions of thermodynamically-feasible metabolic reconstructions. Our model makes use of only: i) a nutrient's structure and function, ii) the presence of a small number of enzymes in the organism, and iii) the carbon flow in pathways that catabolize nutrients. When applied to test organisms, our model allows us to predict whether a nutrient can be a carbon source with an accuracy of about 90% with respect to in silico experiments. In addition, our model provides excellent predictions of whether a medium will produce more or less growth than another (p<10(-6)) and good predictions of the actual value of the in silico biomass production.

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Geographical breakdown

Country Count As %
Spain 2 5%
Iran, Islamic Republic of 1 3%
United States 1 3%
Unknown 33 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 24%
Student > Ph. D. Student 8 22%
Professor > Associate Professor 5 14%
Student > Bachelor 3 8%
Student > Doctoral Student 3 8%
Other 8 22%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 41%
Physics and Astronomy 5 14%
Biochemistry, Genetics and Molecular Biology 4 11%
Engineering 4 11%
Computer Science 4 11%
Other 3 8%
Unknown 2 5%