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High Cooperativity in Negative Feedback can Amplify Noisy Gene Expression

Overview of attention for article published in Bulletin of Mathematical Biology, April 2018
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Title
High Cooperativity in Negative Feedback can Amplify Noisy Gene Expression
Published in
Bulletin of Mathematical Biology, April 2018
DOI 10.1007/s11538-018-0438-y
Pubmed ID
Authors

Pavol Bokes, Yen Ting Lin, Abhyudai Singh

Abstract

Burst-like synthesis of protein is a significant source of cell-to-cell variability in protein levels. Negative feedback is a common example of a regulatory mechanism by which such stochasticity can be controlled. Here we consider a specific kind of negative feedback, which makes bursts smaller in the excess of protein. Increasing the strength of the feedback may lead to dramatically different outcomes depending on a key parameter, the noise load, which is defined as the squared coefficient of variation the protein exhibits in the absence of feedback. Combining stochastic simulation with asymptotic analysis, we identify a critical value of noise load: for noise loads smaller than critical, the coefficient of variation remains bounded with increasing feedback strength; contrastingly, if the noise load is larger than critical, the coefficient of variation diverges to infinity in the limit of ever greater feedback strengths. Interestingly, feedbacks with lower cooperativities have higher critical noise loads, suggesting that they can be preferable for controlling noisy proteins.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 33%
Student > Doctoral Student 3 20%
Researcher 2 13%
Student > Bachelor 1 7%
Student > Postgraduate 1 7%
Other 0 0%
Unknown 3 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 20%
Engineering 3 20%
Immunology and Microbiology 1 7%
Mathematics 1 7%
Neuroscience 1 7%
Other 1 7%
Unknown 5 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 April 2019.
All research outputs
#14,136,687
of 23,096,849 outputs
Outputs from Bulletin of Mathematical Biology
#594
of 1,105 outputs
Outputs of similar age
#178,896
of 326,627 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#13
of 29 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,105 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 326,627 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.