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Mutation and Selection in Bacteria: Modelling and Calibration
Temporal evolution of a clonal bacterial population is modelled taking into account reversible mutation and selection mechanisms. For the mutation model, an efficient algorithm is proposed to verify whether experimental data can be explained by this model. The selection–mutation model has unobservab...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373360/ https://www.ncbi.nlm.nih.gov/pubmed/30430330 http://dx.doi.org/10.1007/s11538-018-0529-9 |
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author | Bayliss, C. D. Fallaize, C. Howitt, R. Tretyakov, M. V. |
author_facet | Bayliss, C. D. Fallaize, C. Howitt, R. Tretyakov, M. V. |
author_sort | Bayliss, C. D. |
collection | PubMed |
description | Temporal evolution of a clonal bacterial population is modelled taking into account reversible mutation and selection mechanisms. For the mutation model, an efficient algorithm is proposed to verify whether experimental data can be explained by this model. The selection–mutation model has unobservable fitness parameters, and, to estimate them, we use an Approximate Bayesian Computation algorithm. The algorithms are illustrated using in vitro data for phase variable genes of Campylobacter jejuni. |
format | Online Article Text |
id | pubmed-6373360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-63733602019-03-01 Mutation and Selection in Bacteria: Modelling and Calibration Bayliss, C. D. Fallaize, C. Howitt, R. Tretyakov, M. V. Bull Math Biol Article Temporal evolution of a clonal bacterial population is modelled taking into account reversible mutation and selection mechanisms. For the mutation model, an efficient algorithm is proposed to verify whether experimental data can be explained by this model. The selection–mutation model has unobservable fitness parameters, and, to estimate them, we use an Approximate Bayesian Computation algorithm. The algorithms are illustrated using in vitro data for phase variable genes of Campylobacter jejuni. Springer US 2018-11-14 2019 /pmc/articles/PMC6373360/ /pubmed/30430330 http://dx.doi.org/10.1007/s11538-018-0529-9 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Bayliss, C. D. Fallaize, C. Howitt, R. Tretyakov, M. V. Mutation and Selection in Bacteria: Modelling and Calibration |
title | Mutation and Selection in Bacteria: Modelling and Calibration |
title_full | Mutation and Selection in Bacteria: Modelling and Calibration |
title_fullStr | Mutation and Selection in Bacteria: Modelling and Calibration |
title_full_unstemmed | Mutation and Selection in Bacteria: Modelling and Calibration |
title_short | Mutation and Selection in Bacteria: Modelling and Calibration |
title_sort | mutation and selection in bacteria: modelling and calibration |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373360/ https://www.ncbi.nlm.nih.gov/pubmed/30430330 http://dx.doi.org/10.1007/s11538-018-0529-9 |
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