Universal constraints on selection strength in lineage trees

Genthon, Arthur; Lacoste, David

doi:10.1101/2020.12.04.412106

Cited by 2 publications

(2 citation statements)

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“…Powell’s results have been generalized in several directions since [5,8,9]. The problem was recently reformulated by Nozoe et al [10] in terms of two different samplings of lineages in a population tree, and their framework has been used to further investigate the lineage-population bias [11,12], its consequences on selection strength [13], and has been analysed through the lens of stochastic thermodynamics [5,14]. The lineage-population bias for any cell trait can be studied using the notion of fitness landscape [10], which quantifies the correlations between the value of the trait and the number of divisions undergone by the cell.…”

Section: Introductionmentioning

confidence: 99%

Analytical cell size distribution: lineage-population bias and parameter inference

Genthon

2022

J. R. Soc. Interface.

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We derive analytical steady-state cell size distributions for size-controlled cells in single-lineage experiments, such as the mother machine, which are fundamentally different from batch cultures where populations of cells grow freely. For exponential single-cell growth, characterizing most bacteria, the lineage-population bias is obtained explicitly. In addition, if volume is evenly split between the daughter cells at division, we show that cells are on average smaller in populations than in lineages. For more general power-law growth rates and deterministic volume partitioning, both symmetric and asymmetric, we derive the exact lineage distribution. This solution is in good agreement with Escherichia coli mother machine data and can be used to infer cell cycle parameters such as the strength of the size control and the asymmetry of the division. When introducing stochastic volume partitioning, we derive the large-size and small-size tails of the lineage distribution and show that the lineage-population bias only depends on the single-cell growth rate. These asymptotic behaviours are extended to the adder model of cell size control. When considering noisy single-cell growth rate, we derive the large-size lineage and population distributions. Finally, we show that introducing noise, either on the volume partitioning or on the single-cell growth rate, can cancel the lineage-population bias.

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Section: Introductionmentioning

confidence: 99%

Analytical cell size distribution: lineage-population bias and parameter inference

Genthon

2022

J. R. Soc. Interface.

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“…Powell's results have been generalized in several directions since [5,8,9]. The problem was recently reformulated by Nozoe et al [10] in terms of two different samplings of lineages in a population tree, and their framework has been used to further investigate the lineage-population bias [11,12], its consequences on selection strength [13], and has been analyzed through the lens of stochastic thermodynamics [5,14]. The lineage-population bias for any cell trait can be studied using the notion of fitness landscape [10], which quantifies the correlations between the value of the trait and the number of divisions undergone by the cell.…”

Section: Introductionmentioning

confidence: 99%

Analytical cell size distribution: lineage-population bias and parameter inference

Genthon¹

2022

Preprint

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Solving population balance equations, we derive analytical steady-state cell size distributions for single-lineage experiments, such as the mother machine. These experiments are fundamentally different from batch cultures where populations of cells grow freely, and the statistical bias between them is obtained by comparing our results to cell size distributions measured in population. For exponential single-cell growth, characterizing most bacteria, the lineage-population bias is obtained explicitly. In addition, if volume is evenly split between the daughter cells at division, we show that cells are on average smaller in populations. For more general power-law growth rates and deterministic volume partitioning, both symmetric and asymmetric, we derive the exact lineage distribution. This solution is in good agreement with E. Coli mother machine data, and can be used to infer cell cycle parameters, such as the strength of the size control and the asymmetry of the division. When introducing stochastic volume partitioning, we derive the large-size and smallsize tails of the lineage size distributions, and show that they respectively do not depend on the partitioning of volume and on the size control strength. Finally, we show that introducing noise, either on the volume partitioning or on the single cell growth rate, can cancel the lineage-population bias.

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Universal constraints on selection strength in lineage trees

Cited by 2 publications

References 25 publications

Analytical cell size distribution: lineage-population bias and parameter inference

Analytical cell size distribution: lineage-population bias and parameter inference

Analytical cell size distribution: lineage-population bias and parameter inference

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