Multistability and dynamic transitions of intracellular Min protein patterns.

Enzo Kingma,Fabai Wu,Erwin Frey,Jacob Halatek,Matthias Reiter,Cees Dekker

doi:10.15252/msb.20156724

Abstract

Cells owe their internal organization to self‐organized protein patterns, which originate and adapt to growth and external stimuli via a process that is as complex as it is little understood. Here, we study the emergence, stability, and state transitions of multistable Min protein oscillation patterns in live Escherichia coli bacteria during growth up to defined large dimensions. De novo formation of patterns from homogenous starting conditions is observed and studied both experimentally and in simulations. A new theoretical approach is developed for probing pattern stability under perturbations. Quantitative experiments and simulations show that, once established, Min oscillations tolerate a large degree of intracellular heterogeneity, allowing distinctly different patterns to persist in different cells with the same geometry. Min patterns maintain their axes for hours in experiments, despite imperfections, expansion, and changes in cell shape during continuous cell growth. Transitions between multistable Min patterns are found to be rare events induced by strong intracellular perturbations. The instances of multistability studied here are the combined outcome of boundary growth and strongly nonlinear kinetics, which are characteristic of the reaction–diffusion patterns that pervade biology at many scales.

Highlights

While we focus on the latter aspect in the main text, we review in Box 1 how, more generally, a Turing instability facilitates symmetry breaking in a planar geometry, which may help the reader to understand why the interconnection between geometry and the classical Turing mechanism is crucial
These patterns exhibit persistent adaptation during cell growth, as well as dynamic transitions induced by strong spatial perturbations
Systematic stability analyses of multistable states in silico revealed that the underlying Min pattern dynamics is set by (i) the sensitivity of initial pattern selection to cellular heterogeneity and (ii) the robustness of the established oscillations in the face of perturbations

Summary

Introduction

Spontaneous emergence of spatial structures from initially homogeneous conditions is a major paradigm in biology, and Alan Turing’s reaction–diffusion theory was the first to show how local chemical interactions could be coupled through diffusion to yield sustained, non-uniform patterns (Turing, 1952). In this way, the symmetry of the starting system can be broken. How patterns change in response to noise and perturbations, be they chemical or geometrical, is poorly understood Resolution of such issues is critical for an understanding of the role of reaction–diffusion systems in the context of the spatial confines and physiology of a cell (or an organism). Focusing on pattern formation from homogeneity is not enough, as was noted by Turing himself at the end of his seminal article in 1952 (Turing, 1952): “Most of an organism, most of the time, is developing from one pattern into another, rather than from homogeneity into a pattern”

Methods

Results

Conclusion