Max Planck Institute for Dynamics and Self-Organisation, 37077 Göttingen, Germany.
Department of Bionanoscience, Delft University of Technology, 2629 HZ Delft, The Netherlands.
Proc Natl Acad Sci U S A. 2022 Jul 12;119(28):e2201250119. doi: 10.1073/pnas.2201250119. Epub 2022 Jul 6.
Phase separation has emerged as an essential concept for the spatial organization inside biological cells. However, despite the clear relevance to virtually all physiological functions, we understand surprisingly little about what phases form in a system of many interacting components, like in cells. Here we introduce a numerical method based on physical relaxation dynamics to study the coexisting phases in such systems. We use our approach to optimize interactions between components, similar to how evolution might have optimized the interactions of proteins. These evolved interactions robustly lead to a defined number of phases, despite substantial uncertainties in the initial composition, while random or designed interactions perform much worse. Moreover, the optimized interactions are robust to perturbations, and they allow fast adaption to new target phase counts. We thus show that genetically encoded interactions of proteins provide versatile control of phase behavior. The phases forming in our system are also a concrete example of a robust emergent property that does not rely on fine-tuning the parameters of individual constituents.
相分离已成为生物细胞内空间组织的一个基本概念。然而，尽管它与几乎所有生理功能都有明显的相关性，但对于在许多相互作用的成分（如细胞）组成的系统中形成的相，我们的了解却惊人地少。在这里，我们引入了一种基于物理松弛动力学的数值方法来研究此类系统中的共存相。我们使用这种方法来优化组件之间的相互作用，类似于进化可能优化了蛋白质之间的相互作用。这些进化后的相互作用可以稳定地导致确定数量的相，尽管初始组成存在很大的不确定性，而随机或设计的相互作用则表现得差得多。此外，优化后的相互作用对干扰具有鲁棒性，并且它们允许快速适应新的目标相数。因此，我们表明，蛋白质的遗传编码相互作用提供了对相行为的多功能控制。我们系统中形成的相也是一种稳健的涌现性质的具体例子，这种性质不依赖于微调单个成分的参数。
