Modeling and Prediction of Single-Cell Galvanotaxis Dynamics Using Machine Learning
Abstract
It has long been known that many types of cells migrate in response to naturally-generated electric fields, and it has been suggested that the external application of an electric field may be used to intervene in and optimize natural processes such as wound healing. Precise cell guidance suitable for such optimization may rely on predictive models of cell migration, which are yet to be developed. Here, we present a deep learning model that can make predictions about the future directedness of cells given a timeseries of previous directedness and electric field values. This model can accept arbitrary electric field values, and we demonstrate that it can be used to perform in silico studies by simulating cell migration lines. Additionally, we show that our modeling approach can be used for a variety of cell types and experimental conditions with very limited training data using transfer learning methods. This predictive approach provides accurate models of cell migration which are suitable for use in control mechanisms with applications in precision medicine.