Fluctuation analysis: can estimates be trusted?

The estimation of mutation rates and relative fitnesses in fluctuation analysis is based on the unrealistic hypothesis that the single-cell times to division are exponentially distributed. Using the classical Luria-Delbrück distribution outside its modelling hypotheses induces an important bias on t...

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Autor principal:	Bernard Ycart
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f815483004e943b5be958c02484acabe
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spelling	oai:doaj.org-article:f815483004e943b5be958c02484acabe2021-11-18T08:42:58ZFluctuation analysis: can estimates be trusted?1932-620310.1371/journal.pone.0080958https://doaj.org/article/f815483004e943b5be958c02484acabe2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24349026/?tool=EBIhttps://doaj.org/toc/1932-6203The estimation of mutation rates and relative fitnesses in fluctuation analysis is based on the unrealistic hypothesis that the single-cell times to division are exponentially distributed. Using the classical Luria-Delbrück distribution outside its modelling hypotheses induces an important bias on the estimation of the relative fitness. The model is extended here to any division time distribution. Mutant counts follow a generalization of the Luria-Delbrück distribution, which depends on the mean number of mutations, the relative fitness of normal cells compared to mutants, and the division time distribution of mutant cells. Empirical probability generating function techniques yield precise estimates both of the mean number of mutations and the relative fitness of normal cells compared to mutants. In the case where no information is available on the division time distribution, it is shown that the estimation procedure using constant division times yields more reliable results. Numerical results both on observed and simulated data are reported.Bernard YcartPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e80958 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bernard Ycart Fluctuation analysis: can estimates be trusted?
description	The estimation of mutation rates and relative fitnesses in fluctuation analysis is based on the unrealistic hypothesis that the single-cell times to division are exponentially distributed. Using the classical Luria-Delbrück distribution outside its modelling hypotheses induces an important bias on the estimation of the relative fitness. The model is extended here to any division time distribution. Mutant counts follow a generalization of the Luria-Delbrück distribution, which depends on the mean number of mutations, the relative fitness of normal cells compared to mutants, and the division time distribution of mutant cells. Empirical probability generating function techniques yield precise estimates both of the mean number of mutations and the relative fitness of normal cells compared to mutants. In the case where no information is available on the division time distribution, it is shown that the estimation procedure using constant division times yields more reliable results. Numerical results both on observed and simulated data are reported.
format	article
author	Bernard Ycart
author_facet	Bernard Ycart
author_sort	Bernard Ycart
title	Fluctuation analysis: can estimates be trusted?
title_short	Fluctuation analysis: can estimates be trusted?
title_full	Fluctuation analysis: can estimates be trusted?
title_fullStr	Fluctuation analysis: can estimates be trusted?
title_full_unstemmed	Fluctuation analysis: can estimates be trusted?
title_sort	fluctuation analysis: can estimates be trusted?
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/f815483004e943b5be958c02484acabe
work_keys_str_mv	AT bernardycart fluctuationanalysiscanestimatesbetrusted
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Fluctuation analysis: can estimates be trusted?

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