Model comparison and assessment for single particle tracking in biological fluids

TL;DR

This paper introduces a Bayesian framework for comparing stochastic models of particle motion in biological fluids, demonstrating that the Generalized Langevin Equation better fits experimental data than fractional Brownian motion.

Contribution

It develops a novel Bayesian methodology for model comparison and inference in particle tracking, applied to distinguish between fBM and GLE models in biological fluids.

Findings

GLE model outperforms fBM in data fitting

Bayesian approach enables rigorous model assessment

Results support viscoelastic GLE as a better descriptor

Abstract

State-of-the-art techniques in passive particle-tracking microscopy provide high-resolution path trajectories of diverse foreign particles in biological fluids. For particles on the order of 1 micron diameter, these paths are generally inconsistent with simple Brownian motion. Yet, despite an abundance of data confirming these findings and their wide-ranging scientific implications, stochastic modeling of the complex particle motion has received comparatively little attention. Even among posited models, there is virtually no literature on likelihood-based inference, model comparisons, and other quantitative assessments. In this article, we develop a rigorous and computationally efficient Bayesian methodology to address this gap. We analyze two of the most prevalent candidate models for 30 second paths of 1 micron diameter tracer particles in human lung mucus: fractional Brownian motion (fBM) and a Generalized Langevin Equation (GLE) consistent with viscoelastic theory. Our model comparisons distinctly favor GLE over fBM, with the former describing the data remarkably well up to the timescales for which we have reliable information.