  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.

Soft Condensed Matter