A framework for modeling and inferring tracer diffusion in crowded environments

TL;DR

This paper introduces a computational framework combining simulations and machine learning to efficiently model and infer tracer diffusion behaviors in crowded biological and soft matter environments.

Contribution

It develops a minimal simulation incorporating steric and hydrodynamic effects and trains a Gaussian process model for rapid MSD prediction based on environmental parameters.

Findings

Transition from diffusive to confined motion with increasing matrix density

The PPGP model predicts MSDs efficiently from structural variables

Minimal model captures tracer MSDs in cellular environments

Abstract

Tracer diffusion in crowded environments is central to many biological and soft matter systems, but quantitative frameworks for linking tracer motion to environmental structure remain limited. Here, we study the transport of rigid tracers in suspensions of soft particles and within living cells. Experiments reveal a transition from diffusive to confined motion as the matrix area fraction increases. We develop a minimal simulation that incorporates steric exclusion and hydrodynamic hindrance to reproduce the observed mean-squared displacements (MSDs). Using simulation outputs, we train a parallel partial Gaussian process (PPGP) model that rapidly predicts MSDs from matrix geometric variables, including area fraction, particle size, and polydispersity. The PPGP model accelerates predictions by several orders of magnitude relative to simulation and experiments. Analysis reveals that tracer transport is primarily governed by accessible pore sizes and that distinct global structures can produce indistinguishable MSDs. We find that the minimal model can also capture the MSDs of internalized tracer particles in cells. The framework enables rapid inference of structural properties in crowded environments, including transport in the intracellular environment.