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Flux Design: In silico design of cell factories based on correlation of pathway fluxes to desired properties

Overview of attention for article published in BMC Systems Biology, December 2009
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
2 blogs

Citations

dimensions_citation
77 Dimensions

Readers on

mendeley
181 Mendeley
citeulike
3 CiteULike
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Title
Flux Design: In silico design of cell factories based on correlation of pathway fluxes to desired properties
Published in
BMC Systems Biology, December 2009
DOI 10.1186/1752-0509-3-120
Pubmed ID
Authors

Guido Melzer, Manely Eslahpazir Esfandabadi, Ezequiel Franco-Lara, Christoph Wittmann

Abstract

The identification of genetic target genes is a key step for rational engineering of production strains towards bio-based chemicals, fuels or therapeutics. This is often a difficult task, because superior production performance typically requires a combination of multiple targets, whereby the complex metabolic networks complicate straightforward identification. Recent attempts towards target prediction mainly focus on the prediction of gene deletion targets and therefore can cover only a part of genetic modifications proven valuable in metabolic engineering. Efficient in silico methods for simultaneous genome-scale identification of targets to be amplified or deleted are still lacking.

Mendeley readers

The data shown below were compiled from readership statistics for 181 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 5 3%
Netherlands 2 1%
United Kingdom 2 1%
China 2 1%
Iran, Islamic Republic of 2 1%
Sweden 2 1%
Kenya 1 <1%
India 1 <1%
Portugal 1 <1%
Other 4 2%
Unknown 159 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 27%
Researcher 37 20%
Student > Master 33 18%
Student > Doctoral Student 12 7%
Professor > Associate Professor 8 4%
Other 24 13%
Unknown 19 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 95 52%
Biochemistry, Genetics and Molecular Biology 27 15%
Engineering 17 9%
Chemical Engineering 4 2%
Computer Science 4 2%
Other 7 4%
Unknown 27 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 23 January 2012.
All research outputs
#2,081,851
of 18,787,703 outputs
Outputs from BMC Systems Biology
#66
of 1,127 outputs
Outputs of similar age
#21,423
of 233,100 outputs
Outputs of similar age from BMC Systems Biology
#6
of 41 outputs
Altmetric has tracked 18,787,703 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,127 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 94% 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 233,100 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 90% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.