Modeling Cell Migration with Convolutional Neural Network and Deep Reinforcement Learning
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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1543193
- Resource Relation:
- Conference: ICML 2019 Workshop on Computational Biology - Long Island, California, United States of America - 6/10/2019 8:00:00 AM-6/14/2019 8:00:00 AM
- Country of Publication:
- United States
- Language:
- English
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