Evolved interactions stabilize many coexisting phases in multicomponent liquids

TL;DR

This paper introduces a numerical method to study phase coexistence in multicomponent liquids, demonstrating how evolved interactions can robustly control phase behavior, with implications for understanding biological cell organization.

Contribution

The authors develop a physical relaxation dynamics-based numerical approach to optimize interactions, revealing how evolution-like processes can produce robust, adaptable phase separation in complex systems.

Findings

Optimized interactions lead to a fixed number of phases despite initial uncertainties.

Evolved interactions are robust to perturbations and enable quick adaptation.

Random or designed interactions perform worse than evolved ones.

Abstract

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 new 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.