Universal Protein Distributions in a Model of Cell Growth and Division

TL;DR

This paper introduces a stochastic growth-and-division model that accurately captures the universal skewed shape and quadratic variance-mean relationship of protein distributions in bacteria and yeast, providing insights into cellular protein variability.

Contribution

The authors develop an exact analytical model of protein distributions in cell growth and division, revealing a single parameter that determines distribution shape and variance-to-mean relation.

Findings

Model reproduces observed protein distribution shapes

Single parameter controls distribution properties

Predictions align with experimental data

Abstract

Protein distributions measured under a broad set of conditions in bacteria and yeast were shown to exhibit a common skewed shape, with variances depending quadratically on means. For bacteria these properties were reproduced by temporal measurements of protein content, showing accumulation and division across generations. Here we present a stochastic growth-and-division model with feedback which captures these observed properties. The limiting copy number distribution is calculated exactly, and a single parameter is found to determine the distribution shape and the variance-to-mean relation. Estimating this parameter from bacterial temporal data reproduces the measured distribution shape with high accuracy, and leads to predictions for future experiments.