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Effects of promoter leakage on dynamics of gene expression

Overview of attention for article published in BMC Systems Biology, March 2015
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Title
Effects of promoter leakage on dynamics of gene expression
Published in
BMC Systems Biology, March 2015
DOI 10.1186/s12918-015-0157-z
Pubmed ID
Authors

Lifang Huang, Zhanjiang Yuan, Peijiang Liu, Tianshou Zhou

Abstract

Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes. As network motifs occurring recurrently in complex biological networks, gene auto-regulatory circuits have been extensively studied but gene expression dynamics remain to be fully understood, e.g., how promoter leakage affects expression noise is unclear. In this work, we analyze a gene model with auto regulation, where the promoter is assumed to have one active state with highly efficient transcription and one inactive state with very lowly efficient transcription (termed as promoter leakage). We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics. Interestingly, we find that promoter leakage always reduces expression noise and that increasing the leakage rate tends to simplify phenotypes. In addition, higher leakage results in fewer bursts. Our results reveal the essential role of promoter leakage in controlling expression dynamics and further phenotype. Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
United Kingdom 1 <1%
China 1 <1%
Unknown 132 97%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 27 20%
Researcher 25 18%
Student > Ph. D. Student 23 17%
Student > Master 20 15%
Student > Doctoral Student 7 5%
Other 9 7%
Unknown 25 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 44 32%
Agricultural and Biological Sciences 28 21%
Medicine and Dentistry 5 4%
Engineering 5 4%
Physics and Astronomy 4 3%
Other 21 15%
Unknown 29 21%
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 19 April 2015.
All research outputs
#18,410,971
of 22,805,349 outputs
Outputs from BMC Systems Biology
#834
of 1,142 outputs
Outputs of similar age
#192,203
of 262,802 outputs
Outputs of similar age from BMC Systems Biology
#14
of 18 outputs
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So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.