  The deformation of cellular membranes regulates trafficking processes, such
as exocytosis and endocytosis. Classically, the Helfrich continuum model is
used to characterize the forces and mechanical parameters that cells tune to
accomplish membrane shape changes. While this classical model effectively
captures curvature generation, one of the core challenges in using it to
approximate a biological process is selecting a set of mechanical parameters
(including bending modulus and membrane tension) from a large set of reasonable
values. We used the Helfrich model to generate a large synthetic dataset from a
random sampling of realistic mechanical parameters and used this dataset to
train machine learning models. These models produced promising results,
accurately classifying model behavior and predicting membrane shape from
mechanical parameters. We also note emerging methods in machine learning that
can leverage the physical insight of the Helfrich model to improve performance
and draw greater insight into how cells control membrane shape change.
