Zyxin is all you need: machine learning adherent cell mechanics
This work addresses the challenge of inferring large-scale physical properties of cells for understanding biophysical processes like adhesion and migration, serving as a case study for integrating neural networks in predictive phenomenological models in cell biology.
The researchers tackled the problem of predicting cellular forces from molecular components by developing a data-driven biophysical modeling approach, showing that images of a single protein (zyxin) can accurately predict forces and generalize to unseen regimes, with models revealing forces are encoded by two length scales in protein distributions.
Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. No systematic strategy currently exists to infer large-scale physical properties of a cell from its many molecular components. This is a significant obstacle to understanding biophysical processes such as cell adhesion and migration. Here, we develop a data-driven biophysical modeling approach to learn the mechanical behavior of adherent cells. We first train neural networks to predict forces generated by adherent cells from images of cytoskeletal proteins. Strikingly, experimental images of a single focal adhesion protein, such as zyxin, are sufficient to predict forces and generalize to unseen biological regimes. This protein field alone contains enough information to yield accurate predictions even if forces themselves are generated by many interacting proteins. We next develop two approaches - one explicitly constrained by physics, the other more agnostic - that help construct data-driven continuum models of cellular forces using this single focal adhesion field. Both strategies consistently reveal that cellular forces are encoded by two different length scales in adhesion protein distributions. Beyond adherent cell mechanics, our work serves as a case study for how to integrate neural networks in the construction of predictive phenomenological models in cell biology, even when little knowledge of the underlying microscopic mechanisms exist.