Fluctuation relations and fitness landscapes of growing cell populations

Abstract We construct a pathwise formulation of a growing population of cells, based on two different samplings of lineages within the population, namely the forward and backward samplings. We show that a general symmetry relation, called fluctuation relation relates these two samplings, independent...

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Autores principales: Arthur Genthon, David Lacoste
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Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/96eb7db0ddd241ec98cd1eb956a9ba71
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spelling oai:doaj.org-article:96eb7db0ddd241ec98cd1eb956a9ba712021-12-02T15:32:59ZFluctuation relations and fitness landscapes of growing cell populations10.1038/s41598-020-68444-x2045-2322https://doaj.org/article/96eb7db0ddd241ec98cd1eb956a9ba712020-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-68444-xhttps://doaj.org/toc/2045-2322Abstract We construct a pathwise formulation of a growing population of cells, based on two different samplings of lineages within the population, namely the forward and backward samplings. We show that a general symmetry relation, called fluctuation relation relates these two samplings, independently of the model used to generate divisions and growth in the cell population. These relations lead to estimators of the population growth rate, which can be very efficient as we demonstrate by an analysis of a set of mother machine data. These fluctuation relations lead to general and important inequalities between the mean number of divisions and the doubling time of the population. We also study the fitness landscape, a concept based on the two samplings mentioned above, which quantifies the correlations between a phenotypic trait of interest and the number of divisions. We obtain explicit results when the trait is the age or the size, for age and size-controlled models.Arthur GenthonDavid LacosteNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Arthur Genthon
David Lacoste
Fluctuation relations and fitness landscapes of growing cell populations
description Abstract We construct a pathwise formulation of a growing population of cells, based on two different samplings of lineages within the population, namely the forward and backward samplings. We show that a general symmetry relation, called fluctuation relation relates these two samplings, independently of the model used to generate divisions and growth in the cell population. These relations lead to estimators of the population growth rate, which can be very efficient as we demonstrate by an analysis of a set of mother machine data. These fluctuation relations lead to general and important inequalities between the mean number of divisions and the doubling time of the population. We also study the fitness landscape, a concept based on the two samplings mentioned above, which quantifies the correlations between a phenotypic trait of interest and the number of divisions. We obtain explicit results when the trait is the age or the size, for age and size-controlled models.
format article
author Arthur Genthon
David Lacoste
author_facet Arthur Genthon
David Lacoste
author_sort Arthur Genthon
title Fluctuation relations and fitness landscapes of growing cell populations
title_short Fluctuation relations and fitness landscapes of growing cell populations
title_full Fluctuation relations and fitness landscapes of growing cell populations
title_fullStr Fluctuation relations and fitness landscapes of growing cell populations
title_full_unstemmed Fluctuation relations and fitness landscapes of growing cell populations
title_sort fluctuation relations and fitness landscapes of growing cell populations
publisher Nature Portfolio
publishDate 2020
url https://doaj.org/article/96eb7db0ddd241ec98cd1eb956a9ba71
work_keys_str_mv AT arthurgenthon fluctuationrelationsandfitnesslandscapesofgrowingcellpopulations
AT davidlacoste fluctuationrelationsandfitnesslandscapesofgrowingcellpopulations
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