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

Genthon, Arthur

doi:10.1098/rsif.2022.0405

Cited by 10 publications

(4 citation statements)

References 54 publications

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“…Recent mathematical models have proposed cell-size based division rates 36 , multi-stages 13,19 and back transitions 4 to explain the division strategy. However, none of them can account for all the observed properties of cell division 24 .…”

Section: Modeling Division In Rod-shaped Bacteriamentioning

confidence: 99%

Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder

Nieto,

Vargas-García,

Pedraza

et al. 2024

npj Syst Biol Appl

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Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: degradation of the precursor protein and two models where the propensity for accumulation depends on the cell size: a nonlinear accumulation rate, and accumulation starting at a threshold size termed the commitment size. These models fit the mean trends but predict different distributions given the birth size. To quantify the precision of the models to explain the data, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. We found that none of the models alone can consistently explain the data. However, the degradation model better explains the division strategy when cells are larger, whereas size-related models (power-law and commitment size) account for smaller cells. Our methodology proposes a data-based method in which different mechanisms can be tested systematically.

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Section: Modeling Division In Rod-shaped Bacteriamentioning

confidence: 99%

Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder

Nieto,

Vargas-García,

Pedraza

et al. 2024

npj Syst Biol Appl

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“…From a theoretical perspective, it is common to define division as a stochastic jump process that occurs at a given rate [43, 44]. A rate-based model considers the division to occur with a given propensity similarly to chemical reactions.…”

Section: Cell Proliferation Strategies: Timer Versus Addermentioning

confidence: 99%

Coupling Cell Size Regulation and Proliferation Dynamics ofC. glutamicumReveals Cell Division Based on Surface Area

Nieto,

Täuber,

Blöbaum

et al. 2023

Preprint

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Single cells actively coordinate growth and division to regulate their size, yet how this size homeostasis at the single-cell level propagates over multiple generations to impact clonal expansion remains fundamentally unexplored. Classicaltimermodels for cell proliferation (where the duration of the cell cycle is an independent variable) predict that the stochastic variation in colony size will increase monotonically over time. In stark contrast, implementing size control according toadderstrategy (where on average a fixed size added from cell birth to division) leads to colony size variations that eventually decay to zero. While these results assume a fixed size of the colony-initiating progenitor cell, further analysis reveals that the magnitude of the intercolony variation in population number is sensitive to heterogeneity in the initial cell size. We validate these predictions by tracking the growth of isogenic microcolonies ofCorynebacterium glutamicumin microfluidic chambers. Approximating their cell shape to a capsule, we observe that the degree of random variability in cell size is different depending on whether the cell size is quantified as per length, surface area, or volume, but size control remains an adder regardless of these size metrics. A comparison of the observed variability in the colony population with the predictions suggests that proliferation matches better with a cell division based on the cell surface. In summary, our integrated mathematical-experimental approach bridges the paradigms of single-cell size regulation and clonal expansion at the population levels. This innovative approach provides elucidation of the mechanisms of size homeostasis from the stochastic dynamics of colony size for rod-shaped microbes.

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“…Predictions on dynamical changes in cell size statistics can then be tested with experimental tracking of individual cell sizes over time and used for inferring processes underlying cell size control. Finally, we would like to compare and contrast cell size dynamics in a single cell over time to that of an expanding cell colony, and prior work has identified differences in size statistics between the two frameworks [47], [50].…”

Section: Equal Transition Ratesmentioning

confidence: 99%

Statistical properties of dynamical models underlying cell size homeostasis

Nieto,

Vargas,

Singh

2023

Preprint

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In this contribution, we use the framework of discrete-event dynamical systems to model a fundamental aspect of life - the proliferation of cells. This process starts with a newborn cell, whose size (as quantified by its volume or mass) increases exponentially over time. At a prescribed time during cellular growth, a cell-division event is triggered that results in the mother cell dividing symmetrically/asymmetrically into newborn cells, and the cycle repeats. Division events are assumed to occur probabilistically at a rate that is a monotonically increasing function of cell size. We derive closed-form expressions for the steady-state moments (mean, variance, skewness) of cell size, and these results are exact when the cell-division events occur at a rate proportional to size. This latter case leads to the recently observed adder-based size control in several types of bacteria, where a fixed size is added between birth and division irrespective of the newborn size. Analytical moment formulas are extended to the biologically relevant scenario where the time between two successive division events is further divided into multiple discrete stages with size-dependent stage transitions. Exact moment computations show a decrease in cell size noise with the increasing number of stages, with a lesser degree of noise attenuation when there are partitioning errors in cell size during division. Development of these results will facilitate the estimation of model parameters from measured cell size distributions and drive an understanding of control mechanisms regulating cell size homeostasis.

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

Cited by 10 publications

References 54 publications

Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder

Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder

Coupling Cell Size Regulation and Proliferation Dynamics ofC. glutamicumReveals Cell Division Based on Surface Area

Statistical properties of dynamical models underlying cell size homeostasis

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