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Quantification of viral infection dynamics in animal experiments
Analyzing the time-course of several viral infections using mathematical models based on experimental data can provide important quantitative insights regarding infection dynamics. Over the past decade, the importance and significance of mathematical modeling has been gaining recognition among virol...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767920/ https://www.ncbi.nlm.nih.gov/pubmed/24058361 http://dx.doi.org/10.3389/fmicb.2013.00264 |
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author | Iwami, Shingo Koizumi, Yoshiki Ikeda, Hiroki Kakizoe, Yusuke |
author_facet | Iwami, Shingo Koizumi, Yoshiki Ikeda, Hiroki Kakizoe, Yusuke |
author_sort | Iwami, Shingo |
collection | PubMed |
description | Analyzing the time-course of several viral infections using mathematical models based on experimental data can provide important quantitative insights regarding infection dynamics. Over the past decade, the importance and significance of mathematical modeling has been gaining recognition among virologists. In the near future, many animal models of human-specific infections and experimental data from high-throughput techniques will become available. This will provide us with the opportunity to develop new quantitative approaches, combining experimental and mathematical analyses. In this paper, we review the various quantitative analyses of viral infections and discuss their possible applications. |
format | Online Article Text |
id | pubmed-3767920 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-37679202013-09-20 Quantification of viral infection dynamics in animal experiments Iwami, Shingo Koizumi, Yoshiki Ikeda, Hiroki Kakizoe, Yusuke Front Microbiol Microbiology Analyzing the time-course of several viral infections using mathematical models based on experimental data can provide important quantitative insights regarding infection dynamics. Over the past decade, the importance and significance of mathematical modeling has been gaining recognition among virologists. In the near future, many animal models of human-specific infections and experimental data from high-throughput techniques will become available. This will provide us with the opportunity to develop new quantitative approaches, combining experimental and mathematical analyses. In this paper, we review the various quantitative analyses of viral infections and discuss their possible applications. Frontiers Media S.A. 2013-09-10 /pmc/articles/PMC3767920/ /pubmed/24058361 http://dx.doi.org/10.3389/fmicb.2013.00264 Text en Copyright © Iwami, Koizumi, Ikeda and Kakizoe. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Iwami, Shingo Koizumi, Yoshiki Ikeda, Hiroki Kakizoe, Yusuke Quantification of viral infection dynamics in animal experiments |
title | Quantification of viral infection dynamics in animal experiments |
title_full | Quantification of viral infection dynamics in animal experiments |
title_fullStr | Quantification of viral infection dynamics in animal experiments |
title_full_unstemmed | Quantification of viral infection dynamics in animal experiments |
title_short | Quantification of viral infection dynamics in animal experiments |
title_sort | quantification of viral infection dynamics in animal experiments |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767920/ https://www.ncbi.nlm.nih.gov/pubmed/24058361 http://dx.doi.org/10.3389/fmicb.2013.00264 |
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