Abstract
Cell migration modeling is a longstanding biological challenge, which is regulated by a highly complex set of regulatory mechanisms at multiple scales in a developmental system. This study presents a generic framework for regulatory mechanisms discovery during cell migration. This framework uses convolutional neural networks and reinforcement learning to better study navigation rules and mechanisms during cell migration. This framework adopts a flexible model-free approach that directly takes raw images as the sensory input. It can better handle simulation scenarios that involve cell division during embryogenesis. Computational experiments also prove that this model achieves better performance than a previous model with a fully connected neural network.
