Stochastic switching between multistable oscillation patterns of the Min-system

TL;DR

This study combines simulations and experiments to analyze how the Min-protein system in E. coli exhibits stochastic switching between multiple stable oscillation patterns influenced by cell geometry, revealing limits of geometry sensing.

Contribution

It introduces particle-based simulations to predict oscillation pattern frequencies and identifies new patterns and relationships in three-dimensional geometries, advancing understanding of stochastic pattern switching.

Findings

Simulation results match experimental oscillation frequencies.

Identified novel oscillation patterns in 3D compartments.

Discovered a linear relation between Min-protein density and volume-to-surface ratio.

Abstract

The spatiotemporal oscillation patterns of the proteins MinD and MinE are used by the bacterium E. coli to sense its own geometry. Strikingly, both computer simulations and experiments have recently shown that for the same geometry of the reaction volume, different oscillation patterns can be stable, with stochastic switching between them. Here we use particle-based Brownian dynamics simulations to predict the relative frequency of different oscillation patterns over a large range of three-dimensional compartment geometries, in excellent agreement with experimental results. Fourier analyses as well as pattern recognition algorithms are used to automatically identify the different oscillation patterns and the switching rates between them. We also identify novel oscillation patterns in three-dimensional compartments with membrane-covered walls and identify a linear relation between the bound Min-protein densities and the volume-to-surface ratio. In general, our work shows how geometry sensing is limited by multistability and stochastic fluctuations.