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RobOKoD: microbial strain design for (over)production of target compounds

Overview of attention for article published in Frontiers in Cell and Developmental Biology, March 2015
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Title
RobOKoD: microbial strain design for (over)production of target compounds
Published in
Frontiers in Cell and Developmental Biology, March 2015
DOI 10.3389/fcell.2015.00017
Pubmed ID
Authors

Natalie J. Stanford, Pierre Millard, Neil Swainston

Abstract

Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.

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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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
China 1 2%
Unknown 62 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 22%
Student > Ph. D. Student 13 20%
Student > Master 9 14%
Student > Bachelor 6 9%
Student > Doctoral Student 4 6%
Other 10 16%
Unknown 8 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 31%
Engineering 12 19%
Biochemistry, Genetics and Molecular Biology 11 17%
Computer Science 6 9%
Chemical Engineering 1 2%
Other 2 3%
Unknown 12 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 21 January 2016.
All research outputs
#19,760,214
of 24,284,650 outputs
Outputs from Frontiers in Cell and Developmental Biology
#5,393
of 9,881 outputs
Outputs of similar age
#198,135
of 267,544 outputs
Outputs of similar age from Frontiers in Cell and Developmental Biology
#18
of 20 outputs
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So far Altmetric has tracked 9,881 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.