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The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

Overview of attention for article published in BMC Bioinformatics, April 2017
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
25 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
118 Mendeley
citeulike
3 CiteULike
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Title
The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism
Published in
BMC Bioinformatics, April 2017
DOI 10.1186/s12859-017-1615-y
Pubmed ID
Authors

Garrett W. Birkel, Amit Ghosh, Vinay S. Kumar, Daniel Weaver, David Ando, Tyler W. H. Backman, Adam P. Arkin, Jay D. Keasling, Héctor García Martín

Abstract

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and (13)C Metabolic Flux Analysis. Moreover, it introduces the capability to use (13)C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale (13)C Metabolic Flux Analysis (2S-(13)C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Twitter Demographics

The data shown below were collected from the profiles of 25 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 <1%
United States 1 <1%
Denmark 1 <1%
China 1 <1%
Unknown 114 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 25%
Student > Ph. D. Student 26 22%
Student > Master 14 12%
Student > Doctoral Student 7 6%
Student > Bachelor 7 6%
Other 21 18%
Unknown 14 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 23%
Agricultural and Biological Sciences 26 22%
Computer Science 10 8%
Engineering 10 8%
Environmental Science 5 4%
Other 17 14%
Unknown 23 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 17 October 2020.
All research outputs
#1,538,896
of 22,962,258 outputs
Outputs from BMC Bioinformatics
#304
of 7,306 outputs
Outputs of similar age
#32,474
of 309,562 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 116 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,306 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 95% 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 309,562 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 89% of its contemporaries.
We're also able to compare this research output to 116 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.