Non-local fluctuation correlations in active gels

TL;DR

This paper develops a mathematical framework to analyze active fluctuations in biological gels, enabling the characterization of force-generating agents and their effects on the material's mechanical properties through fluctuation correlations.

Contribution

It introduces a novel method to estimate local forces and distinguish different force generators in active gels using fluctuation correlation analysis.

Findings

Identification of three independent fluctuation modes in active gels.

Experimental validation of a high-frequency ballistic regime.

Quantitative estimation of filament tension from fluctuation data.

Abstract

Many active materials and biological systems are driven far from equilibrium by embedded agents that spontaneously generate forces and distort the surrounding material. Probing and characterizing these athermal fluctuations is essential for understanding the properties and behaviors of such systems. Here we present a mathematical procedure to estimate the local action of force-generating agents from the observed fluctuating displacement fields. The active agents are modeled as oriented force dipoles or isotropic compression foci, and the matrix on which they act is assumed to be either a compressible elastic continuum or a coupled network-solvent system. Correlations at a single point and between points separated by an arbitrary distance are obtained, giving a total of three independent fluctuation modes that can be tested with microrheology experiments. Since oriented dipoles and isotropic compression foci give different contributions to these fluctuation modes, ratiometric analysis allows us characterize the force generators. We also predict and experimentally find a high-frequency ballistic regime, arising from individual force generating events in the form of the slow build-up of stress followed by rapid but finite decay. Finally, we provide a quantitative statistical model to estimate the mean filament tension from these athermal fluctuations, which leads to stiffening of active networks.