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Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species

Overview of attention for article published in PLoS Computational Biology, February 2010
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Title
Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species
Published in
PLoS Computational Biology, February 2010
DOI 10.1371/journal.pcbi.1000690
Pubmed ID
Authors

Shiri Freilich, Anat Kreimer, Elhanan Borenstein, Uri Gophna, Roded Sharan, Eytan Ruppin

Abstract

The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic requirements, or directly due to an adaptive selection in favor of genes that allow a species to endure genetic perturbations. Stratifying the individual effects of each origin requires one to study the pertaining evolutionary forces across many species under diverse conditions. Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments. We provide evidence that variations among species in their level of robustness reflect ecological adaptations. We decouple metabolic robustness into two components and quantify the extents of each: the first, environmental-dependent, is responsible for at least 20% of the non-essential reactions and its extent is associated with the species' lifestyle (specialized/generalist); the second, environmental-independent, is associated (correlation = approximately 0.6) with the intrinsic metabolic capacities of a species-higher robustness is observed in fast growers or in organisms with an extensive production of secondary metabolites. Finally, we identify reactions that are uniquely susceptible to perturbations in human pathogens, potentially serving as novel drug-targets.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 10%
Iran, Islamic Republic of 2 2%
Japan 2 2%
Italy 1 <1%
Australia 1 <1%
Belgium 1 <1%
Netherlands 1 <1%
Germany 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 97 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 33%
Researcher 31 26%
Student > Master 14 12%
Professor > Associate Professor 10 8%
Professor 5 4%
Other 13 11%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 73 61%
Biochemistry, Genetics and Molecular Biology 10 8%
Computer Science 10 8%
Engineering 5 4%
Physics and Astronomy 3 3%
Other 6 5%
Unknown 12 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 September 2012.
All research outputs
#17,302,400
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,481
of 8,964 outputs
Outputs of similar age
#85,009
of 102,516 outputs
Outputs of similar age from PLoS Computational Biology
#42
of 52 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.