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A Mathematical Model of CR3/TLR2 Crosstalk in the Context of Francisella tularensis Infection

Overview of attention for article published in PLoS Computational Biology, November 2012
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Title
A Mathematical Model of CR3/TLR2 Crosstalk in the Context of Francisella tularensis Infection
Published in
PLoS Computational Biology, November 2012
DOI 10.1371/journal.pcbi.1002757
Pubmed ID
Authors

Rachel Leander, Shipan Dai, Larry S. Schlesinger, Avner Friedman

Abstract

Complement Receptor 3 (CR3) and Toll-like Receptor 2 (TLR2) are pattern recognition receptors expressed on the surface of human macrophages. Although these receptors are essential components for recognition by the innate immune system, pathogen coordinated crosstalk between them can suppress the production of protective cytokines and promote infection. Recognition of the virulent Schu S4 strain of the intracellular pathogen Francisella tularensis by host macrophages involves CR3/TLR2 crosstalk. Although experimental data provide evidence that Lyn kinase and PI3K are essential components of the CR3 pathway that influences TLR2 activity, additional responsible upstream signaling components remain unknown. In this paper we construct a mathematical model of CR3 and TLR2 signaling in response to F. tularensis. After demonstrating that the model is consistent with experimental results we perform numerical simulations to evaluate the contributions that Akt and Ras-GAP make to ERK inhibition. The model confirms that phagocytosis-associated changes in the composition of the cell membrane can inhibit ERK activity and predicts that Akt and Ras-GAP synergize to inhibit ERK.

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 %
Hungary 1 3%
Brazil 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 30%
Researcher 8 27%
Student > Postgraduate 3 10%
Student > Doctoral Student 2 7%
Student > Bachelor 1 3%
Other 3 10%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 33%
Immunology and Microbiology 6 20%
Mathematics 4 13%
Biochemistry, Genetics and Molecular Biology 2 7%
Business, Management and Accounting 1 3%
Other 2 7%
Unknown 5 17%
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 27 November 2012.
All research outputs
#22,938,588
of 25,576,801 outputs
Outputs from PLoS Computational Biology
#8,612
of 9,003 outputs
Outputs of similar age
#181,827
of 202,608 outputs
Outputs of similar age from PLoS Computational Biology
#100
of 107 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,003 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 1st percentile – i.e., 1% 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 202,608 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.