Understanding cytoskeletal avalanches using mechanical stability analysis

TL;DR

This study uses simulations and machine learning to analyze the mechanical energy fluctuations in the cytoskeleton, revealing avalanche-like events and providing insights into the structural susceptibility of cellular networks.

Contribution

It introduces a minimal cytoskeletal model that links energy fluctuations to collective filament displacements and uses machine learning to predict cytoquakes from vibrational spectra.

Findings

Non-Gaussian energy fluctuation statistics observed

Large energy release events correlate with collective filament displacements

Mechanical instability can predict cytoquake occurrence

Abstract

Eukaryotic cells are mechanically supported by a polymer network called the cytoskeleton, which consumes chemical energy to dynamically remodel its structure. Recent experiments in vivo have revealed that this remodeling occasionally happens through anomalously large displacements, reminiscent of earthquakes or avalanches. These cytoskeletal avalanches might indicate that the cytoskeleton's structural response to a changing cellular environment is highly sensitive, and they are therefore of significant biological interest. However, the physics underlying "cytoquakes" is poorly understood. Here, we use agent-based simulations of cytoskeletal self-organization to study fluctuations in the network's mechanical energy. We robustly observe non-Gaussian statistics and asymmetrically large rates of energy release compared to accumulation in a minimal cytoskeletal model. The large events of energy release are found to correlate with large, collective displacements of the cytoskeletal filaments. We also find that the changes in the localization of tension and the projections of the network motion onto the vibrational normal modes are asymmetrically distributed for energy release and accumulation. These results imply an avalanche-like process of slow energy storage punctuated by fast, large events of energy release involving a collective network rearrangement. We further show that mechanical instability precedes cytoquake occurrence through a machine learning model that dynamically forecasts cytoquakes using the vibrational spectrum as input. Our results provide the first connection between the cytoquake phenomenon and the network's mechanical energy and can help guide future investigations of the cytoskeleton's structural susceptibility.