Cell Migration

Cell migration research focuses on understanding the mechanisms and dynamics of cell movement, aiming to decipher the underlying rules governing this fundamental biological process. Current investigations leverage advanced machine learning techniques, including graph neural networks, neural ordinary differential equations, and deep learning-based regression models, to analyze complex spatiotemporal data from microscopy videos and single-cell sequencing. These computational approaches enable more accurate tracking of cell trajectories, inference of cell-cell interactions, and prediction of cell fates, significantly improving our ability to study collective cell behavior and its implications for development and disease. This improved understanding has broad implications for fields such as developmental biology, cancer research, and regenerative medicine.