Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels

Area of Science:

• Soft-matter physics
• Biophysics
• Polymer science

Background:

• Native mucus is a crucial biopolymer-based soft-matter material.
• Understanding pathogen diffusion dynamics within mucus is vital for biological and medical applications.
• Mucus structure, primarily composed of mucins, significantly influences particle transport.

Purpose of the Study:

• To investigate the non-Gaussian and non-ergodic nature of pathogen diffusion in mucus.
• To analyze tracer motion in mucin hydrogels using advanced statistical methods.
• To compare the efficacy of different diffusion models, including Brownian motion and viscoelastic anomalous diffusion.

Main Methods:

• Studied passive, thermally driven motion of micron-sized tracers in mucin hydrogels.
• Employed Bayesian analysis with a nested-sampling algorithm to rank diffusion models.
• Analyzed tracer trajectories using models such as fractional Brownian motion and standard Brownian motion.

Main Results:

• Viscoelastic anomalous diffusion was frequently the most probable model for tracer movement.
• Brownian motion was the second most likely model, while diffusing diffusivity was rarely observed.
• Diffusion exhibited increased non-Gaussianity and non-ergodicity at lower pH, correlating with more heterogeneous mucin networks.

Conclusions:

• The diffusion of tracers in mucin gels is best described by viscoelastic anomalous diffusion, highlighting complex dynamics.
• Low pH conditions lead to the most significant deviations from simple diffusion, indicating network-dependent transport.
• The study provides new insights into the physical mechanisms governing diffusion in mucin gels using a robust Bayesian framework.