↓ Skip to main content

A new genome-scale metabolic model of Corynebacterium glutamicum and its application

Overview of attention for article published in Biotechnology for Biofuels and Bioproducts, June 2017
Altmetric Badge

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)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

news
2 news outlets
patent
1 patent

Citations

dimensions_citation
73 Dimensions

Readers on

mendeley
100 Mendeley
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 new genome-scale metabolic model of Corynebacterium glutamicum and its application
Published in
Biotechnology for Biofuels and Bioproducts, June 2017
DOI 10.1186/s13068-017-0856-3
Pubmed ID
Authors

Yu Zhang, Jingyi Cai, Xiuling Shang, Bo Wang, Shuwen Liu, Xin Chai, Tianwei Tan, Yun Zhang, Tingyi Wen

Abstract

Corynebacterium glutamicum is an important platform organism for industrial biotechnology to produce amino acids, organic acids, bioplastic monomers, and biofuels. The metabolic flexibility, broad substrate spectrum, and fermentative robustness of C. glutamicum make this organism an ideal cell factory to manufacture desired products. With increases in gene function, transport system, and metabolic profile information under certain conditions, developing a comprehensive genome-scale metabolic model (GEM) of C. glutamicum ATCC13032 is desired to improve prediction accuracy, elucidate cellular metabolism, and guide metabolic engineering. Here, we constructed a new GEM for ATCC13032, iCW773, consisting of 773 genes, 950 metabolites, and 1207 reactions. Compared to the previous model, iCW773 supplemented 496 gene-protein-reaction associations, refined five lumped reactions, balanced the mass and charge, and constrained the directionality of reactions. The simulated growth rates of C. glutamicum cultivated on seven different carbon sources using iCW773 were consistent with experimental values. Pearson's correlation coefficient between the iCW773-simulated and experimental fluxes was 0.99, suggesting that iCW773 provided an accurate intracellular flux distribution of the wild-type strain growing on glucose. Furthermore, genetic interventions for overproducing l-lysine, 1,2-propanediol and isobutanol simulated using OptForceMUST were in accordance with reported experimental results, indicating the practicability of iCW773 for the design of metabolic networks to overproduce desired products. In vivo genetic modifications of iCW773-predicted targets resulted in the de novo generation of an l-proline-overproducing strain. In fed-batch culture, the engineered C. glutamicum strain produced 66.43 g/L l-proline in 60 h with a yield of 0.26 g/g (l-proline/glucose) and a productivity of 1.11 g/L/h. To our knowledge, this is the highest titer and productivity reported for l-proline production using glucose as the carbon resource in a minimal medium. Our developed iCW773 serves as a high-quality platform for model-guided strain design to produce industrial bioproducts of interest. This new GEM will be a successful multidisciplinary tool and will make valuable contributions to metabolic engineering in academia and industry.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 28%
Student > Master 18 18%
Student > Bachelor 13 13%
Researcher 6 6%
Other 4 4%
Other 10 10%
Unknown 21 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 36 36%
Agricultural and Biological Sciences 25 25%
Engineering 5 5%
Environmental Science 3 3%
Chemical Engineering 3 3%
Other 4 4%
Unknown 24 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 22 July 2021.
All research outputs
#2,089,777
of 25,382,440 outputs
Outputs from Biotechnology for Biofuels and Bioproducts
#83
of 1,578 outputs
Outputs of similar age
#39,401
of 327,487 outputs
Outputs of similar age from Biotechnology for Biofuels and Bioproducts
#6
of 52 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,578 research outputs from this source. They receive a mean Attention Score of 4.9. 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 327,487 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 52 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.