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

TL;DR

This paper derives analytical distributions of cell sizes in single-lineage experiments, compares them to population data, and explores how division and growth parameters influence size bias and distribution tails.

Contribution

It provides explicit formulas for lineage-population bias, analyzes effects of division asymmetry and stochasticity, and offers methods to infer cell cycle parameters from data.

Findings

Lineage-population bias is explicitly characterized for exponential growth.

Cells are smaller in populations when volume is evenly split at division.

Noise in volume partitioning or growth rate can cancel the bias.

Abstract

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 small-size 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.