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Modeling the architecture of the regulatory system controlling methylenomycin production in Streptomyces coelicolor

Overview of attention for article published in Journal of Biological Engineering, October 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Title
Modeling the architecture of the regulatory system controlling methylenomycin production in Streptomyces coelicolor
Published in
Journal of Biological Engineering, October 2017
DOI 10.1186/s13036-017-0071-6
Pubmed ID
Authors

Jack E. Bowyer, Emmanuel LC. de los Santos, Kathryn M. Styles, Alex Fullwood, Christophe Corre, Declan G. Bates

Abstract

The antibiotic methylenomycin A is produced naturally by Streptomyces coelicolor A3(2), a model organism for streptomycetes. This compound is of particular interest to synthetic biologists because all of the associated biosynthetic, regulatory and resistance genes are located on a single cluster on the SCP1 plasmid, making the entire module easily transferable between different bacterial strains. Understanding further the regulation and biosynthesis of the methylenomycin producing gene cluster could assist in the identification of motifs that can be exploited in synthetic regulatory systems for the rational engineering of novel natural products and antibiotics. We identify and validate a plausible architecture for the regulatory system controlling methylenomycin production in S. coelicolor using mathematical modeling approaches. Model selection via an approximate Bayesian computation (ABC) approach identifies three candidate model architectures that are most likely to produce the available experimental data, from a set of 48 possible candidates. Subsequent global optimization of the parameters of these model architectures identifies a single model that most accurately reproduces the dynamical response of the system, as captured by time series data on methylenomycin production. Further analyses of variants of this model architecture that capture the effects of gene knockouts also reproduce qualitative experimental results observed in mutant S. coelicolor strains. The mechanistic mathematical model developed in this study recapitulates current biological knowledge of the regulation and biosynthesis of the methylenomycin producing gene cluster, and can be used in future studies to make testable predictions and formulate experiments to further improve our understanding of this complex regulatory system.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 20%
Researcher 5 17%
Student > Ph. D. Student 4 13%
Other 3 10%
Student > Master 2 7%
Other 2 7%
Unknown 8 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 30%
Agricultural and Biological Sciences 6 20%
Chemistry 3 10%
Unspecified 1 3%
Mathematics 1 3%
Other 0 0%
Unknown 10 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 October 2017.
All research outputs
#5,048,627
of 24,383,935 outputs
Outputs from Journal of Biological Engineering
#77
of 289 outputs
Outputs of similar age
#84,256
of 327,045 outputs
Outputs of similar age from Journal of Biological Engineering
#2
of 10 outputs
Altmetric has tracked 24,383,935 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 289 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has gotten more attention than average, scoring higher than 73% 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 327,045 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 74% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.