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Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses

Overview of attention for article published in PLoS Computational Biology, June 2012
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Title
Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses
Published in
PLoS Computational Biology, June 2012
DOI 10.1371/journal.pcbi.1002588
Pubmed ID
Authors

Kasia A. Pawelek, Giao T. Huynh, Michelle Quinlivan, Ann Cullinane, Libin Rong, Alan S. Perelson

Abstract

Influenza virus infection remains a public health problem worldwide. The mechanisms underlying viral control during an uncomplicated influenza virus infection are not fully understood. Here, we developed a mathematical model including both innate and adaptive immune responses to study the within-host dynamics of equine influenza virus infection in horses. By comparing modeling predictions with both interferon and viral kinetic data, we examined the relative roles of target cell availability, and innate and adaptive immune responses in controlling the virus. Our results show that the rapid and substantial viral decline (about 2 to 4 logs within 1 day) after the peak can be explained by the killing of infected cells mediated by interferon activated cells, such as natural killer cells, during the innate immune response. After the viral load declines to a lower level, the loss of interferon-induced antiviral effect and an increased availability of target cells due to loss of the antiviral state can explain the observed short phase of viral plateau in which the viral level remains unchanged or even experiences a minor second peak in some animals. An adaptive immune response is needed in our model to explain the eventual viral clearance. This study provides a quantitative understanding of the biological factors that can explain the viral and interferon kinetics during a typical influenza virus infection.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 220 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
Italy 2 <1%
Australia 1 <1%
Germany 1 <1%
India 1 <1%
Brazil 1 <1%
Belgium 1 <1%
United Kingdom 1 <1%
Unknown 206 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 25%
Researcher 47 21%
Professor > Associate Professor 16 7%
Student > Bachelor 16 7%
Student > Master 15 7%
Other 37 17%
Unknown 34 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 63 29%
Mathematics 21 10%
Immunology and Microbiology 19 9%
Biochemistry, Genetics and Molecular Biology 15 7%
Medicine and Dentistry 15 7%
Other 46 21%
Unknown 41 19%