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

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.

References in corpus (4)

• From cellular properties to population asymptotics in the Population Balance Equation
• Universal constraints on selection strength in lineage trees
• Generalized Euler-Lotka equation for correlated cell divisions
• Well posedness and stochastic derivation of a diffusion-growth-fragmentation equation in a chemostat

Cited by in corpus (4)

• Asymptotic decoupling of population growth rate and cell size distribution
• From noisy cell size control to population growth: when variability can be beneficial
• Competition between transient oscillations and early stochasticity in exponentially growing populations
• Cell lineage statistics with incomplete population trees