On distinguishability among cell-division models based on population and single-cell-level distributions

TL;DR

This paper explores how various statistical properties and distributions can be used to distinguish between different cell-division models and growth types, revealing both differentiating factors and indistinguishability under certain conditions.

Contribution

It introduces new statistical criteria for differentiating cell-division models and growth types, and demonstrates the robustness of population distributions across models.

Findings

Different statistical properties can distinguish models and growth types.

Population distributions are indistinguishable across models despite different rules.

Theoretical predictions are supported by simulations and experimental data.

Abstract

It is well known that the different cell-division models, such as Timer, Sizer, and Adder, can be distinguished based on the correlations between different single-cell-level quantities such as birth-size, division-time, division-size, and division-added-size. Here, we show that other statistical properties of these quantities can also be used to distinguish between them. Additionally, the statistical relationships and different correlation patterns can also differentiate between the different types of single-cell growth, such as linear and exponential. Further, we demonstrate that various population-level distributions, such as age, size, and added-size distributions, are indistinguishable across different models of cell division despite them having different division rules and correlation patterns. Moreover, this indistinguishability is robust to stochasticity in growth rate and holds for both exponential and linear growth. Finally, we show that our theoretical predictions are corroborated by simulations and supported by existing single-cell experimental data.