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Reverse-Engineering Post-Transcriptional Regulation of Gap Genes in Drosophila melanogaster

Overview of attention for article published in PLoS Computational Biology, October 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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2 news outlets
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7 X users
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1 Facebook page

Citations

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39 Dimensions

Readers on

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57 Mendeley
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Title
Reverse-Engineering Post-Transcriptional Regulation of Gap Genes in Drosophila melanogaster
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003281
Pubmed ID
Authors

Kolja Becker, Eva Balsa-Canto, Damjan Cicin-Sain, Astrid Hoermann, Hilde Janssens, Julio R. Banga, Johannes Jaeger

Abstract

Systems biology proceeds through repeated cycles of experiment and modeling. One way to implement this is reverse engineering, where models are fit to data to infer and analyse regulatory mechanisms. This requires rigorous methods to determine whether model parameters can be properly identified. Applying such methods in a complex biological context remains challenging. We use reverse engineering to study post-transcriptional regulation in pattern formation. As a case study, we analyse expression of the gap genes Krüppel, knirps, and giant in Drosophila melanogaster. We use detailed, quantitative datasets of gap gene mRNA and protein expression to solve and fit a model of post-transcriptional regulation, and establish its structural and practical identifiability. Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels. Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation. This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
United States 1 2%
Unknown 55 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 28%
Student > Ph. D. Student 11 19%
Student > Bachelor 6 11%
Professor > Associate Professor 4 7%
Student > Doctoral Student 3 5%
Other 11 19%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 39%
Biochemistry, Genetics and Molecular Biology 8 14%
Computer Science 5 9%
Engineering 4 7%
Physics and Astronomy 4 7%
Other 7 12%
Unknown 7 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 17 March 2017.
All research outputs
#1,825,775
of 25,706,302 outputs
Outputs from PLoS Computational Biology
#1,569
of 9,024 outputs
Outputs of similar age
#16,540
of 226,794 outputs
Outputs of similar age from PLoS Computational Biology
#26
of 153 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,024 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.2. This one has done well, scoring higher than 82% 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 226,794 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 92% of its contemporaries.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.