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Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions

Overview of attention for article published in BMC Bioinformatics, July 2000
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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 (94th percentile)

Mentioned by

blogs
2 blogs
patent
1 patent

Citations

dimensions_citation
228 Dimensions

Readers on

mendeley
339 Mendeley
citeulike
8 CiteULike
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Title
Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions
Published in
BMC Bioinformatics, July 2000
DOI 10.1186/1471-2105-1-1
Pubmed ID
Authors

Jeremy S Edwards, Bernhard O Palsson

Abstract

Genome sequencing and bioinformatics are producing detailed lists of the molecular components contained in many prokaryotic organisms. From this 'parts catalogue' of a microbial cell, in silico representations of integrated metabolic functions can be constructed and analyzed using flux balance analysis (FBA). FBA is particularly well-suited to study metabolic networks based on genomic, biochemical, and strain specific information. Herein, we have utilized FBA to interpret and analyze the metabolic capabilities of Escherichia coli. We have computationally mapped the metabolic capabilities of E. coli using FBA and examined the optimal utilization of the E. coli metabolic pathways as a function of environmental variables. We have used an in silico analysis to identify seven gene products of central metabolism (glycolysis, pentose phosphate pathway, TCA cycle, electron transport system) essential for aerobic growth of E. coli on glucose minimal media, and 15 gene products essential for anaerobic growth on glucose minimal media. The in silico tpi-, zwf, and pta- mutant strains were examined in more detail by mapping the capabilities of these in silico isogenic strains. We found that computational models of E. coli metabolism based on physicochemical constraints can be used to interpret mutant behavior. These in silica results lead to a further understanding of the complex genotype-phenotype relation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 339 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 14 4%
Germany 5 1%
India 3 <1%
Netherlands 2 <1%
France 2 <1%
Ireland 1 <1%
Switzerland 1 <1%
Lithuania 1 <1%
Sweden 1 <1%
Other 9 3%
Unknown 300 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 98 29%
Researcher 63 19%
Student > Master 50 15%
Student > Bachelor 28 8%
Professor > Associate Professor 21 6%
Other 49 14%
Unknown 30 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 163 48%
Biochemistry, Genetics and Molecular Biology 32 9%
Engineering 27 8%
Computer Science 24 7%
Physics and Astronomy 12 4%
Other 47 14%
Unknown 34 10%
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 20 July 2015.
All research outputs
#2,585,406
of 25,287,709 outputs
Outputs from BMC Bioinformatics
#681
of 7,672 outputs
Outputs of similar age
#2,074
of 38,931 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 25,287,709 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,672 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 91% 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 38,931 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them