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Tytuł pozycji:

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness.

Tytuł :
A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness.
Autorzy :
Sego TJ; Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.; Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America.
Aponte-Serrano JO; Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.; Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America.
Ferrari Gianlupi J; Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.; Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America.
Heaps SR; Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.
Breithaupt K; Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.; Cognitive Science Program, Indiana University, Bloomington, Indiana, United States of America.
Brusch L; Center for Information Services and High Performance Computing (ZIH), Technische Universität, Dresden, Germany.
Crawshaw J; School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
Osborne JM; School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
Quardokus EM; Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.
Plemper RK; Institute for Biomedical Sciences, Georgia State University, Atlanta, Georgia, United States of America.
Glazier JA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.; Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America.
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Źródło :
PLoS computational biology [PLoS Comput Biol] 2020 Dec 21; Vol. 16 (12), pp. e1008451. Date of Electronic Publication: 2020 Dec 21 (Print Publication: 2020).
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Original Publication: San Francisco, CA : Public Library of Science, [2005]-
MeSH Terms :
Epithelium*/immunology
Epithelium*/virology
Models, Immunological*
Virus Diseases*/drug therapy
Virus Diseases*/immunology
Computational Biology/*methods
Antiviral Agents/therapeutic use ; COVID-19/immunology ; Computer Simulation ; Hepacivirus/immunology ; Hepatitis C/drug therapy ; Hepatitis C/immunology ; Humans ; SARS-CoV-2/immunology
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Grant Information :
R01 AI141222 United States AI NIAID NIH HHS; R01 GM122424 United States GM NIGMS NIH HHS; R01 AI153400 United States AI NIAID NIH HHS; R01 AI071002 United States AI NIAID NIH HHS; R01 HD079327 United States HD NICHD NIH HHS; U24 EB028887 United States EB NIBIB NIH HHS
Substance Nomenclature :
0 (Antiviral Agents)
Entry Date(s) :
Date Created: 20201221 Date Completed: 20210113 Latest Revision: 20210508
Update Code :
20210508
PubMed Central ID :
PMC7785254
DOI :
10.1371/journal.pcbi.1008451
PMID :
33347439
Czasopismo naukowe
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
Update of: bioRxiv. 2020 May 07;:. (PMID: 32511367)
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