Nonlinear mechanosensation in fiber networks

In the extracellular matrix, eukaryotic cells exert forces that deform their surroundings. By doing so, they can perform mechanosensation: Cells measure the mechanics of their environment, and adapt their behavior accordingly. Extracellular matrices are, however, disordered nonlinear media: How can a mechanosensor at the cellular scale reliably measure the surroundings mechanics through local probing? Here, we develop a model for nonlinear mechanosensation in disordered fiber networks. At low forces, the linear response of the matrix combined with its extreme mechanical heterogeneity precludes reliable mechanosensation. In contrast, we find that this heterogeneity is strongly suppressed in the physiologically relevant nonlinear mechanical regime where fibers buckle. Conceptually, nonlinearity increases the range of mechanosensation, thereby enhancing disorder averaging and providing more accurate nonlinear mechanical measurements. We support our model using microrheology experiments and show theoretically that this nonlinear mechanosensation is generic to all fiber networks. This contrasts with the collagen-specific observation that nonlinear macroscopic elastic moduli are independent of network density, which we show to originate from the fiber's constitutive nonlinearity. Together, our theoretical study disentangles the micro- and macrorheological nonlinearities of fiber networks, and shows how mechanosensors such as cells can take advantage of these nonlinearities to robustly measure their mechanical environment despite heterogeneities.