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Cell polarization relies on long-range cortical flows, which are driven byactive stresses and resisted by the cytoskeletal network. While the generalmechanisms that contribute to cortical flows are known, a quantitativeunderstanding of the factors that tune flow speeds has remained lacking. Here,we combine physical simulation, representation learning, and theory toelucidate the role of actin turnover in cortical flows. We show how turnovertunes the actin density and filament curvature and use representation learningto demonstrate that these quantities are sufficient to predict cortical flowspeeds. We extend a recent theory for contractility to account for filamentcurvature in addition to the nonuniform distribution of crosslinkers alongactin filaments due to turnover. We obtain formulas that can be used to fitdata from simulations and microscopy experiments. Our work provides insightsinto the mechanisms of contractility that contribute to cortical flows and howthey can be controlled quantitatively.

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