Evolved interactions stabilize many coexisting phases in multicomponent liquids

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.

7 pages, 5 figures, and Supplementary Information

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• Optimization of Non-Equilibrium Self-Assembly Protocols Using Markov State Models
• Liquid Hopfield model: retrieval and localization in multicomponent liquid mixtures
• Enhanced stability and chaotic condensates in multi-species non-reciprocal mixtures
• Programmable phase behavior in fluids with designable interactions
• Nonequilibrium mixture dynamics: A model for mobilities and its consequences
• Mobility-induced kinetic effects in multicomponent mixtures
• The intertwined physics of active chemical reactions and phase separation
• Ensemble inequivalence in the design of mixtures with super-Gibbs phase coexistence