Model-based image analysis of a tethered Brownian fibre for shear stress sensing

TL;DR

This paper introduces a mathematical and statistical framework for estimating shear stress on biological surfaces by analyzing images of a tethered Brownian fibre, advancing beyond previous phenomenological models.

Contribution

It develops a novel model-based image analysis method that reconstructs particle positions from images, enabling mechanistically rational shear stress estimation from experimental data.

Findings

First mechanistically rational analysis of the assay

Framework successfully applied to experimental data

Advances in multidisciplinary image analysis techniques

Abstract

The measurement of shear stress acting on a biologically relevant surface is a challenging problem, particularly in the complex environment of, for example, the vasculature. While an experimental method for the direct detection of wall shear stress via the imaging of a synthetic biology nanorod has recently been developed, the data interpretation so far has been limited to phenomenological random walk modelling, small angle approximation, and image analysis techniques which do not take into account the production of an image from a 3D subject. In this report we develop a mathematical and statistical framework to estimate shear stress from rapid imaging sequences based firstly on stochastic modelling of the dynamics of a tethered Brownian fibre in shear flow, and secondly on novel model-based image analysis, which reconstructs phage positions by solving the inverse problem of image formation. This framework is tested on experimental data, providing the first mechanistically rational analysis of the novel assay. What follows further develops the established theory for an untethered particle in a semi-dilute suspension, which is of relevance to, for example, the study of Brownian nanowires without flow, and presents new ideas in the field of multidisciplinary image analysis.