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Mapping the landscape of metabolic goals of a cell

Overview of attention for article published in Genome Biology, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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24 X users

Citations

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31 Dimensions

Readers on

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133 Mendeley
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Title
Mapping the landscape of metabolic goals of a cell
Published in
Genome Biology, May 2016
DOI 10.1186/s13059-016-0968-2
Pubmed ID
Authors

Qi Zhao, Arion I. Stettner, Ed Reznik, Ioannis Ch. Paschalidis, Daniel Segrè

Abstract

Genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. We introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories.

X Demographics

X Demographics

The data shown below were collected from the profiles of 24 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Sweden 1 <1%
Chile 1 <1%
Japan 1 <1%
United Kingdom 1 <1%
Unknown 125 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 28%
Researcher 27 20%
Student > Master 11 8%
Student > Bachelor 9 7%
Student > Doctoral Student 8 6%
Other 20 15%
Unknown 21 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 28%
Biochemistry, Genetics and Molecular Biology 26 20%
Computer Science 13 10%
Engineering 9 7%
Chemical Engineering 6 5%
Other 9 7%
Unknown 33 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 13 November 2017.
All research outputs
#2,526,293
of 25,374,917 outputs
Outputs from Genome Biology
#2,027
of 4,467 outputs
Outputs of similar age
#42,714
of 348,593 outputs
Outputs of similar age from Genome Biology
#45
of 80 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 348,593 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.