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Optimization of lipid production with a genome-scale model of Yarrowia lipolytica

Overview of attention for article published in BMC Systems Biology, October 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#7 of 1,142)
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
3 news outlets
blogs
1 blog
twitter
2 X users
patent
1 patent

Citations

dimensions_citation
105 Dimensions

Readers on

mendeley
204 Mendeley
citeulike
1 CiteULike
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Title
Optimization of lipid production with a genome-scale model of Yarrowia lipolytica
Published in
BMC Systems Biology, October 2015
DOI 10.1186/s12918-015-0217-4
Pubmed ID
Authors

Martin Kavšček, Govindprasad Bhutada, Tobias Madl, Klaus Natter

Abstract

Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well as for biotechnological applications but they typically occur simultaneously thus lowering the respective yields. Therefore, engineering of strains with high lipid content relies on novel concepts such as computational simulation to better understand the two competing processes and to eliminate citrate excretion. Using a genome-scale model (GSM) of baker's yeast as a scaffold, we reconstructed the metabolic network of Y. lipolytica and optimized it for use in flux balance analysis (FBA), with the aim to simulate growth and lipid production phases of this yeast. We validated our model and found the predictions of the growth behavior of Y. lipolytica in excellent agreement with experimental data. Based on these data, we successfully designed a fed-batch strategy to avoid citrate excretion during the lipid production phase. Further analysis of the network suggested that the oxygen demand of Y. lipolytica is reduced upon induction of lipid synthesis. According to this finding we hypothesized that a reduced aeration rate might induce lipid accumulation. This prediction was indeed confirmed experimentally. In a fermentation combining these two strategies lipid content of the biomass was increased by 80 %, and lipid yield was improved more than four-fold, compared to standard conditions. Genome scale network reconstructions provide a powerful tool to predict the effects of genetic modifications and the metabolic response to environmental conditions. The high accuracy and the predictive value of a newly reconstructed GSM of Y. lipolytica to optimize growth conditions for lipid accumulation are demonstrated. Based on these findings, further strategies for engineering Y. lipolytica towards higher efficiency in single cell oil production are discussed.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 204 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Sweden 1 <1%
France 1 <1%
China 1 <1%
Singapore 1 <1%
Unknown 198 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 23%
Researcher 29 14%
Student > Bachelor 29 14%
Student > Master 20 10%
Student > Doctoral Student 9 4%
Other 22 11%
Unknown 48 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 51 25%
Agricultural and Biological Sciences 46 23%
Engineering 18 9%
Chemical Engineering 11 5%
Computer Science 6 3%
Other 13 6%
Unknown 59 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 33. 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 06 November 2019.
All research outputs
#1,023,360
of 22,831,537 outputs
Outputs from BMC Systems Biology
#7
of 1,142 outputs
Outputs of similar age
#16,944
of 284,375 outputs
Outputs of similar age from BMC Systems Biology
#1
of 35 outputs
Altmetric has tracked 22,831,537 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 99% 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 284,375 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 35 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 97% of its contemporaries.