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Multiscale Modeling of Influenza A Virus Infection Supports the Development of Direct-Acting Antivirals

Overview of attention for article published in PLoS Computational Biology, November 2013
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Title
Multiscale Modeling of Influenza A Virus Infection Supports the Development of Direct-Acting Antivirals
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003372
Pubmed ID
Authors

Frank S. Heldt, Timo Frensing, Antje Pflugmacher, Robin Gröpler, Britta Peschel, Udo Reichl

Abstract

Influenza A viruses are respiratory pathogens that cause seasonal epidemics with up to 500,000 deaths each year. Yet there are currently only two classes of antivirals licensed for treatment and drug-resistant strains are on the rise. A major challenge for the discovery of new anti-influenza agents is the identification of drug targets that efficiently interfere with viral replication. To support this step, we developed a multiscale model of influenza A virus infection which comprises both the intracellular level where the virus synthesizes its proteins, replicates its genome, and assembles new virions and the extracellular level where it spreads to new host cells. This integrated modeling approach recapitulates a wide range of experimental data across both scales including the time course of all three viral RNA species inside an infected cell and the infection dynamics in a cell population. It also allowed us to systematically study how interfering with specific steps of the viral life cycle affects virus production. We find that inhibitors of viral transcription, replication, protein synthesis, nuclear export, and assembly/release are most effective in decreasing virus titers whereas targeting virus entry primarily delays infection. In addition, our results suggest that for some antivirals therapy success strongly depends on the lifespan of infected cells and, thus, on the dynamics of virus-induced apoptosis or the host's immune response. Hence, the proposed model provides a systems-level understanding of influenza A virus infection and therapy as well as an ideal platform to include further levels of complexity toward a comprehensive description of infectious diseases.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 4%
United States 2 2%
Unknown 94 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 28%
Researcher 24 24%
Student > Master 10 10%
Student > Bachelor 7 7%
Professor 5 5%
Other 18 18%
Unknown 8 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 25%
Biochemistry, Genetics and Molecular Biology 12 12%
Chemical Engineering 9 9%
Immunology and Microbiology 7 7%
Engineering 7 7%
Other 25 25%
Unknown 15 15%
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 26 November 2013.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#7,953
of 8,960 outputs
Outputs of similar age
#232,041
of 315,413 outputs
Outputs of similar age from PLoS Computational Biology
#130
of 146 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 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 8th percentile – i.e., 8% 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 315,413 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.