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Reconstruction of a genome-scale metabolic model for Actinobacillus succinogenes 130Z

Overview of attention for article published in BMC Systems Biology, May 2018
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

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Title
Reconstruction of a genome-scale metabolic model for Actinobacillus succinogenes 130Z
Published in
BMC Systems Biology, May 2018
DOI 10.1186/s12918-018-0585-7
Pubmed ID
Authors

Bruno Pereira, Joana Miguel, Paulo Vilaça, Simão Soares, Isabel Rocha, Sónia Carneiro

Abstract

Actinobacillus succinogenes is a promising bacterial catalyst for the bioproduction of succinic acid from low-cost raw materials. In this work, a genome-scale metabolic model was reconstructed and used to assess the metabolic capabilities of this microorganism under producing conditions. The model, iBP722, was reconstructed based on the functional reannotation of the complete genome sequence of A. succinogenes 130Z and manual inspection of metabolic pathways, covering 1072 enzymatic reactions associated with 722 metabolic genes that involve 713 metabolites. The highly curated model was effective in capturing the growth of A. succinogenes on various carbon sources, as well as the SA production under various growth conditions with fair agreement between experimental and predicted data. Calculated flux distributions under different conditions show that a number of metabolic pathways are affected by the activity of some metabolic enzymes at key nodes in metabolism, including the transport mechanism of carbon sources and the ability to fix carbon dioxide. The established genome-scale metabolic model can be used for model-driven strain design and medium alteration to improve succinic acid yields.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 20%
Student > Master 8 15%
Researcher 6 11%
Student > Bachelor 5 9%
Lecturer 2 4%
Other 6 11%
Unknown 16 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 22%
Chemical Engineering 5 9%
Engineering 5 9%
Agricultural and Biological Sciences 5 9%
Computer Science 4 7%
Other 8 15%
Unknown 15 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 29 March 2019.
All research outputs
#6,462,074
of 23,081,466 outputs
Outputs from BMC Systems Biology
#228
of 1,144 outputs
Outputs of similar age
#113,804
of 331,094 outputs
Outputs of similar age from BMC Systems Biology
#8
of 31 outputs
Altmetric has tracked 23,081,466 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 79% 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 331,094 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 65% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.