A reaction network model of microscale liquid-liquid phase separation reveals effects of spatial dimension
Jinyoung Kim, Sean D. Lawley, Jinsu Kim

TL;DR
This study uses mathematical modeling to explore how the spatial dimension (2D vs 3D) influences liquid-liquid phase separation (LLPS) in proteins, revealing that dimension impacts LLPS features beyond diffusion effects.
Contribution
The paper introduces a stochastic reaction network model that explains the effects of spatial dimension on LLPS, independent of diffusion coefficients, aligning with recent experimental observations.
Findings
Model reproduces phase diagram consistent with thermodynamics
Predicts qualitative differences in LLPS due to spatial dimension
Provides new hypotheses on dimension effects beyond diffusion
Abstract
Proteins can form droplets via liquid-liquid phase separation (LLPS) in cells. Recent experiments demonstrate that LLPS is qualitatively different on two-dimensional (2d) surfaces compared to three-dimensional (3d) solutions. In this paper, we use mathematical modeling to investigate the causes of the discrepancies between LLPS in 2d versus 3d. We model the number of proteins and droplets inducing LLPS by continuous-time Markov chains and use chemical reaction network theory to analyze the model. To reflect the influence of space dimension, droplet formation and dissociation rates are determined using the first hitting times of diffusing proteins. We first show that our stochastic model reproduces the appropriate phase diagram and is consistent with the relevant thermodynamic constraints. After further analyzing the model, we find that it predicts that the space dimension induces…
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Taxonomy
TopicsPickering emulsions and particle stabilization · Innovative Microfluidic and Catalytic Techniques Innovation
MethodsDiffusion
