How accurate is mechanobiology? A statistical test of cell force

TL;DR

This paper highlights the lack of error quantification in cell force measurements in mechanobiology and proposes a new statistical framework to improve the reliability and hypothesis testing of these measurements.

Contribution

It introduces a general reconstruction framework for cell force measurements, addressing the absence of error estimates and enabling statistical hypothesis testing.

Findings

Identifies the absence of error bars in cell force measurements.

Proposes a statistical reconstruction framework.

Enables hypothesis testing in mechanobiology experiments.

Abstract

Mechanobiology is gaining more and more traction as the fundamental role of physical forces in biological function becomes clearer. Forces at the microscale are often measured indirectly using inverse problems such as Traction Force Microscopy because biological experiments are hard to access with physical probes. In contrast with the experimental nature of biology and physics, these measurements do not come with error bars, confidence regions, or p-values. The aim of this manuscript is to publicize this issue and to propose a first step towards a remedy therefor in the form of a general reconstruction framework. We also show that this opens the door to hypothesis testing of seemingly abstract experimental questions.