Elucidating the Role of Filament Turnover in Cortical Flow using Simulations and Representation Learning

TL;DR

This study combines simulations, representation learning, and theory to quantitatively understand how actin filament turnover influences cortical flow speeds in cell polarization.

Contribution

It introduces a new theoretical framework incorporating filament curvature and actin turnover, validated by simulations and microscopy data.

Findings

Filament turnover affects actin density and curvature.

Representation learning predicts cortical flow speeds from actin properties.

Extended theory accounts for filament curvature and turnover effects.

Abstract

Cell polarization relies on long-range cortical flows, which are driven by active stresses and resisted by the cytoskeletal network. While the general mechanisms that contribute to cortical flows are known, a quantitative understanding of the factors that tune flow speeds has remained lacking. Here, we combine physical simulation, representation learning, and theory to elucidate the role of actin turnover in cortical flows. We show how turnover tunes the actin density and filament curvature and use representation learning to demonstrate that these quantities are sufficient to predict cortical flow speeds. We extend a recent theory for contractility to account for filament curvature in addition to the nonuniform distribution of crosslinkers along actin filaments due to turnover. We obtain formulas that can be used to fit data from simulations and microscopy experiments. Our work provides insights into the mechanisms of contractility that contribute to cortical flows and how they can be controlled quantitatively.