Ren Ziyuan, Weyer Henrik, Sandler Michael, Würthner Laeschkir, Fu Haochen, Tangtartharakul Chanin B, Li Dongyang, Sou Cindy, Villarreal Daniel, Kim Judy E, Frey Erwin, Jun Suckjoon
Department of Physics, University of California San Diego, La Jolla, CA USA.
Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, Germany.
Nat Phys. 2025;21(7):1160-1169. doi: 10.1038/s41567-025-02878-w. Epub 2025 May 5.
The Min protein system prevents abnormal cell division in bacteria by forming oscillatory patterns between cell poles. However, predicting the protein concentrations at which oscillations start and whether cells can maintain them under physiological perturbations remains challenging. Here we show that dynamic pattern formation is robust across a wide range of Min protein levels and variations in the growth physiology using genetically engineered strains. We modulate the expression of and under fast- and slow-growth conditions and build a MinD versus MinE phase diagram that reveals dynamic patterns, including travelling and standing waves. We found that the natural expression level of Min proteins is resource-optimal and robust to changes in protein concentration. In addition, we observed an invariant wavelength of dynamic Min patterns across the phase diagram. We explain the experimental findings quantitatively with biophysical theory based on reaction-diffusion models that consider the switching of MinE between its latent and active states, indicating its essential role as a robustness module for Min oscillation in vivo. Our results underline the potential of integrating quantitative cell physiology and biophysical modelling to understand the fundamental mechanisms controlling cell division machinery, and they offer insights applicable to other biological processes.
Min蛋白系统通过在细胞两极之间形成振荡模式来防止细菌中的异常细胞分裂。然而,预测振荡开始时的蛋白质浓度以及细胞在生理扰动下是否能够维持振荡仍然具有挑战性。在这里,我们表明,使用基因工程菌株,动态模式形成在广泛的Min蛋白水平和生长生理学变化范围内是稳健的。我们在快速和缓慢生长条件下调节MinD和MinE的表达,并构建了一个MinD与MinE相图,该相图揭示了动态模式,包括行波和驻波。我们发现Min蛋白的天然表达水平是资源最优的,并且对蛋白质浓度的变化具有稳健性。此外,我们在整个相图中观察到动态Min模式的波长不变。我们基于反应扩散模型的生物物理理论对实验结果进行了定量解释,该模型考虑了MinE在其潜伏状态和活跃状态之间的切换,表明其作为体内Min振荡的稳健性模块的重要作用。我们的结果强调了整合定量细胞生理学和生物物理建模以理解控制细胞分裂机制的基本机制的潜力,并且它们提供了适用于其他生物过程的见解。