Distinguishing different modes of growth using single-cell data

Kar, Prathitha; Krishnan, Sriram Tiruvadi; Männik, Jaana; Amir, Ariel

doi:10.7554/elife.72565

Cited by 17 publications

(23 citation statements)

References 62 publications

(163 reference statements)

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“…We found that the growth rate of the cell increases by ∼30% during the cell cycle in fast growth conditions (Fig. 1A), consistent with recent reports of increase in E. coli growth rate during the cell cycle [6,30]. This trend in super-exponential growth is preserved across different nutrient conditions (Fig.…”

Section: Model For Cell Growthsupporting

confidence: 91%

Super-exponential growth and stochastic size dynamics in rod-like bacteria

Cylke

Banerjee

2022

Preprint

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Proliferating bacterial cells exhibit stochastic growth and shape dynamics but the regulation of noise in bacterial growth and morphogenesis remains poorly understood. A quantitative understanding of morphogenetic noise control, and how it changes under different growth conditions, would provide better insights into cell-to-cell variability and intergenerational fluctuations in cell physiology. Using multigenerational growth and shape data of single Escherichia coli and Caulobacter crescentus cells, we deduce the equations governing growth and shape dynamics of bacterial cells. Interestingly, we find that both E. coli and C. crescentus cells grow faster than an exponential within a cell cycle, irrespective of nutrient or temperature conditions. We propose a mechanistic model that explains the emergence of super-exponential growth from autocatalytic production of ribosomes, coupled to the rate of cell elongation and surface area synthesis. Using this new model and statistical inference on large datasets, we construct the Langevin equations governing cell size and shape dynamics of E. coli cells in different growth conditions. The single-cell level model predicts how noise in intragenerational and intergenerational processes regulate variability in cell morphology and generation times, revealing quantitative strategies for cellular resource allocation and morphogenetic noise control in different growth conditions.

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Section: Model For Cell Growthsupporting

confidence: 91%

Super-exponential growth and stochastic size dynamics in rod-like bacteria

Cylke

Banerjee

2022

Preprint

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“…Previous studies measured how bacterial cells grow in size by tracking the size of individual cells over time with microscopy; by calculating the slope between the relative cell size at cell division and cell division time various models of how cells regulate the time to divide have been proposed (reviewed in [35]). Kar et al .…”

Section: Discussionmentioning

confidence: 99%

“…Kar et al . [35] challenged these simple regression analyses suggesting that multiple models for cell growth may be consistent with the experimentally measured correlation between cell size and division time; the authors suggested that interpretation of this correlation should be done in terms of a mathematical model of the hypothesized cell division process. Our work suggests a similar approach.…”

Section: Discussionmentioning

confidence: 99%

Correlation between speed and turning naturally arises for sparsely sampled cell movements

Ganusov

Zenkov

Majumder

2021

Preprint

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Mechanisms regulating cell movement are not fully understood. One major feature that may allow cells to displace far from an initial location is persistence, the ability to perform movements in a direction similar to the previous movement direction. The level of cell persistence is determined by the turning angles (angles between two sequential movement vectors). Several recent studies, including one in eLife \cite{Jerison.e20}, found that a cell's average speed and average turning angle are negatively correlated, and suggested a fundamental cell-intrinsic movement program whereby cells with lower turning ability are intrinsically faster. Using simulations, we show that a negative correlation between the measured average cell's speed and average turning angle naturally arises due to sampling when the frequency of sampling is lower than that of the cell's decisions to change movement direction. Interestingly, assuming heterogeneity in the cell's persistence ability (determined by the concentration parameter of the von Mises-Fisher distribution) but not in the cell's speed results in high differences in measured average speed per cell and a strong negative correlation between cell's speed and average turning angle. Our results thus suggest that the observed negative correlation between a cell's speed and turning angle need not arise due to cell's intrinsic program, but can be a simple consequence of experimental measurements. Additional analyses show, however, that while theoretically it is possible to discriminate between the alternatives when recording cell movements at high frequencies, experimentally high imaging frequencies are associated with increased noise, and this represents a major barrier to determine whether a cell-intrinsic correlation between cell's speed and its turning ability truly exists.

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“…Coli grows slightly faster than exponential for large sizes (x > 1), and slightly slower than exponential for small sizes (x < 1). This may be linked to the super-exponential growth observed for E. Coli in [33], where the exponential growth rate ν increases during the cell cycle.…”

Section: B Test On Experimental Data: Parameters Inferencementioning

confidence: 96%

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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Distinguishing different modes of growth using single-cell data

Cited by 17 publications

References 62 publications

Super-exponential growth and stochastic size dynamics in rod-like bacteria

Super-exponential growth and stochastic size dynamics in rod-like bacteria

Correlation between speed and turning naturally arises for sparsely sampled cell movements

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

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