↓ Skip to main content

A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database

Overview of attention for article published in BMC Systems Biology, June 2014
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

twitter
2 X users
patent
1 patent

Citations

dimensions_citation
46 Dimensions

Readers on

mendeley
210 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database
Published in
BMC Systems Biology, June 2014
DOI 10.1186/1752-0509-8-79
Pubmed ID
Authors

Daniel S Weaver, Ingrid M Keseler, Amanda Mackie, Ian T Paulsen, Peter D Karp

Abstract

Constraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 210 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
Germany 1 <1%
Chile 1 <1%
Brazil 1 <1%
Canada 1 <1%
Singapore 1 <1%
Australia 1 <1%
Mexico 1 <1%
Iran, Islamic Republic of 1 <1%
Other 2 <1%
Unknown 193 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 26%
Researcher 46 22%
Student > Master 35 17%
Student > Bachelor 13 6%
Professor 9 4%
Other 24 11%
Unknown 28 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 66 31%
Biochemistry, Genetics and Molecular Biology 44 21%
Engineering 17 8%
Computer Science 12 6%
Chemical Engineering 7 3%
Other 21 10%
Unknown 43 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 26 March 2020.
All research outputs
#7,355,930
of 25,374,647 outputs
Outputs from BMC Systems Biology
#239
of 1,132 outputs
Outputs of similar age
#66,392
of 241,932 outputs
Outputs of similar age from BMC Systems Biology
#2
of 30 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 77% 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 241,932 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.