Liao, LE orcid.org/0000-0001-6069-2633, Carruthers, J orcid.org/0000-0002-1372-3254
, Smither, SJ orcid.org/0000-0002-7835-3581
et al. (12 more authors)
(2020)
Quantification of Ebola virus replication kinetics in vitro.
PLoS Computational Biology, 16 (11).
e1008375.
ISSN 1553-734X
Abstract
Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola virus modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola virus infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.
Metadata
Item Type: | Article |
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Authors/Creators: | This paper has 15 authors. You can scroll the list below to see them all or them all.
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Copyright, Publisher and Additional Information: | This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Oct 2020 16:41 |
Last Modified: | 08 Jan 2025 12:01 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Identification Number: | 10.1371/journal.pcbi.1008375 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167283 |
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