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Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations
The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose–response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose–response model for listeriosis. While descriptive models can account for such vari...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124125/ https://www.ncbi.nlm.nih.gov/pubmed/30225020 http://dx.doi.org/10.1098/rsos.180343 |
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author | Rahman, Ashrafur Munther, Daniel Fazil, Aamir Smith, Ben Wu, Jianhong |
author_facet | Rahman, Ashrafur Munther, Daniel Fazil, Aamir Smith, Ben Wu, Jianhong |
author_sort | Rahman, Ashrafur |
collection | PubMed |
description | The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose–response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose–response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. By contrast, this study exhibits dose–response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen–host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose–response relationships with the underlying bistable dynamics of the model. Relying on in vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen–immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose–response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of Listeria monocytogenes that should be examined. |
format | Online Article Text |
id | pubmed-6124125 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-61241252018-09-17 Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations Rahman, Ashrafur Munther, Daniel Fazil, Aamir Smith, Ben Wu, Jianhong R Soc Open Sci Mathematics The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose–response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose–response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. By contrast, this study exhibits dose–response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen–host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose–response relationships with the underlying bistable dynamics of the model. Relying on in vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen–immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose–response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of Listeria monocytogenes that should be examined. The Royal Society Publishing 2018-08-01 /pmc/articles/PMC6124125/ /pubmed/30225020 http://dx.doi.org/10.1098/rsos.180343 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Mathematics Rahman, Ashrafur Munther, Daniel Fazil, Aamir Smith, Ben Wu, Jianhong Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations |
title | Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations |
title_full | Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations |
title_fullStr | Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations |
title_full_unstemmed | Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations |
title_short | Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations |
title_sort | advancing risk assessment: mechanistic dose–response modelling of listeria monocytogenes infection in human populations |
topic | Mathematics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124125/ https://www.ncbi.nlm.nih.gov/pubmed/30225020 http://dx.doi.org/10.1098/rsos.180343 |
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