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State-of-the-art techniques in passive particle-tracking microscopy providehigh-resolution path trajectories of diverse foreign particles in biologicalfluids. For particles on the order of 1 micron diameter, these paths aregenerally inconsistent with simple Brownian motion. Yet, despite an abundanceof data confirming these findings and their wide-ranging scientificimplications, stochastic modeling of the complex particle motion has receivedcomparatively little attention. Even among posited models, there is virtuallyno literature on likelihood-based inference, model comparisons, and otherquantitative assessments. In this article, we develop a rigorous andcomputationally efficient Bayesian methodology to address this gap. We analyzetwo of the most prevalent candidate models for 30 second paths of 1 microndiameter 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 describingthe data remarkably well up to the timescales for which we have reliableinformation.

Model comparison and assessment for single particle tracking in biological fluids