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Rethinking Transcriptional Activation in the Arabidopsis Circadian Clock

Overview of attention for article published in PLoS Computational Biology, July 2014
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Title
Rethinking Transcriptional Activation in the Arabidopsis Circadian Clock
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003705
Pubmed ID
Authors

Karl Fogelmark, Carl Troein

Abstract

Circadian clocks are biological timekeepers that allow living cells to time their activity in anticipation of predictable daily changes in light and other environmental factors. The complexity of the circadian clock in higher plants makes it difficult to understand the role of individual genes or molecular interactions, and mathematical modelling has been useful in guiding clock research in model organisms such as Arabidopsis thaliana. We present a model of the circadian clock in Arabidopsis, based on a large corpus of published time course data. It appears from experimental evidence in the literature that most interactions in the clock are repressive. Hence, we remove all transcriptional activation found in previous models of this system, and instead extend the system by including two new components, the morning-expressed activator RVE8 and the nightly repressor/activator NOX. Our modelling results demonstrate that the clock does not need a large number of activators in order to reproduce the observed gene expression patterns. For example, the sequential expression of the PRR genes does not require the genes to be connected as a series of activators. In the presented model, transcriptional activation is exclusively the task of RVE8. Predictions of how strongly RVE8 affects its targets are found to agree with earlier interpretations of the experimental data, but generally we find that the many negative feedbacks in the system should discourage intuitive interpretations of mutant phenotypes. The dynamics of the clock are difficult to predict without mathematical modelling, and the clock is better viewed as a tangled web than as a series of loops.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Chile 1 <1%
Portugal 1 <1%
Unknown 120 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 22%
Student > Bachelor 16 13%
Student > Master 15 12%
Researcher 12 10%
Professor 8 7%
Other 23 19%
Unknown 22 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 60 49%
Biochemistry, Genetics and Molecular Biology 20 16%
Mathematics 4 3%
Engineering 3 2%
Environmental Science 2 2%
Other 5 4%
Unknown 29 24%
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 23 July 2014.
All research outputs
#17,302,400
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,481
of 8,964 outputs
Outputs of similar age
#136,722
of 227,561 outputs
Outputs of similar age from PLoS Computational Biology
#124
of 161 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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