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A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189

Overview of attention for article published in PLoS Computational Biology, February 2009
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Title
A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189
Published in
PLoS Computational Biology, February 2009
DOI 10.1371/journal.pcbi.1000285
Pubmed ID
Authors

Patrick F. Suthers, Madhukar S. Dasika, Vinay Satish Kumar, Gennady Denisov, John I. Glass, Costas D. Maranas

Abstract

With a genome size of approximately 580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.

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Geographical breakdown

Country Count As %
United States 9 3%
United Kingdom 8 3%
Germany 4 1%
France 2 <1%
Iran, Islamic Republic of 2 <1%
Russia 2 <1%
Portugal 2 <1%
India 2 <1%
Sweden 1 <1%
Other 5 2%
Unknown 241 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 74 27%
Researcher 66 24%
Student > Master 46 17%
Student > Bachelor 19 7%
Professor > Associate Professor 14 5%
Other 37 13%
Unknown 22 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 142 51%
Biochemistry, Genetics and Molecular Biology 37 13%
Computer Science 18 6%
Engineering 16 6%
Chemical Engineering 8 3%
Other 23 8%
Unknown 34 12%