Machine learning interpretable models of cell mechanics from protein images
M. Schmitt*, J. Colen*, S. Sala, J. Devany, S. Seetharaman, A. Caillier, M.L. Gardel, P.W. Oakes, and V. Vitelli
Published in Cell, 2024
Cell behavior is driven by biochemical processes involving many different proteins. Using machine learning, we investigated links between specific proteins and mechanics and found that one protein, zyxin, is all you need to predict cellular forces. From this, we built interpretable continuum models which captured cell behavior by incorporating biochemical complexity.
Collaboration with Matthew Schmitt (UChicago), Vincenzo Vitelli (UChicago), and Gardel (UChicago) and Oakes (Loyola Chicago) labs.