Modeling membrane curvature generation using mechanics and machine learning

TL;DR

This paper combines classical membrane mechanics with machine learning to classify and predict membrane shape changes, addressing parameter selection challenges in biological modeling.

Contribution

It introduces a machine learning approach trained on synthetic Helfrich model data to classify membrane behavior and predict shapes, enhancing understanding of membrane mechanics.

Findings

Machine learning models accurately classify membrane behavior.

Models can predict membrane shape from mechanical parameters.

Emerging methods leverage physical models for better performance.

Abstract

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.