Maximum Likelihood Estimation for Single Particle, Passive Microrheology Data with Drift

TL;DR

This paper introduces a maximum likelihood estimation method for analyzing passive microrheology data with drift, providing a more accurate alternative to traditional correction techniques, especially in biological fluids with non-diffusive drift.

Contribution

The paper proposes a parametric maximum likelihood approach to simultaneously estimate drift and diffusion parameters from particle path data, improving accuracy over standard correction methods.

Findings

ML method accurately estimates drift and diffusion parameters.

Comparison shows ML approach outperforms traditional correction in high drift scenarios.

Application to biological mucus data demonstrates practical utility.

Abstract

Volume limitations and low yield thresholds of biological fluids have led to widespread use of passive microparticle rheology. The mean-squared-displacement (MSD) statistics of bead position time series (bead paths) are either applied directly to determine the creep compliance [Xu et al (1998)] or transformed to determine dynamic storage and loss moduli [Mason & Weitz (1995)]. A prevalent hurdle arises when there is a non-diffusive experimental drift in the data. Commensurate with the magnitude of drift relative to diffusive mobility, quantified by a P\'eclet number, the MSD statistics are distorted, and thus the path data must be "corrected" for drift. The standard approach is to estimate and subtract the drift from particle paths, and then calculate MSD statistics. We present an alternative, parametric approach using maximum likelihood estimation that simultaneously fits drift and diffusive model parameters from the path data; the MSD statistics (and consequently the compliance and dynamic moduli) then follow directly from the best-fit model. We illustrate and compare both methods on simulated path data over a range of P\'eclet numbers, where exact answers are known. We choose fractional Brownian motion as the numerical model because it affords tunable, sub-diffusive MSD statistics consistent with typical 30 second long, experimental observations of microbeads in several biological fluids. Finally, we apply and compare both methods on data from human bronchial epithelial cell culture mucus.