Driven-dissipative dynamics of active cytoskeletal networks underlie near-critical energy fluctuations

Eukaryotic cells are mechanically supported by a polymer network called the cytoskeleton, which consumes chemical energy to dynamically remodel its structure. Information processing occurs in cytoskeletal networks through the coupling of chemical signaling pathways to structural-mechanical responses which produce useful morphological changes. Recent experiments revealed that cytoskeletal remodeling occasionally happens through unusually large, step-like entropy production events reminiscent of earthquakes. These “cytoquakes" may reflect an optimization of information processing, and are hence of significant theoretical interest. The physics underlying cytoquakes is poorly understood, however, hindering investigation of their possible biological roles. Here we use agent-based simulations with a computational routine for quantifying entropy production to show that cytoquakes' origins lie in the inherent driven-dissipative dynamics of active cytoskeletal networks, similarly to models exhibiting self-organized criticality. Combining machine learning with normal mode decomposition, we show that mechanical instability precedes cytoquakes, which then induce a spatial homogenization of tension sustained by the network.