drl4cellmovement

drl4cellmovement models cell movement during early Caenorhabditis elegans embryogenesis using deep reinforcement learning within an agent-based modeling framework.

Key Features:

• Integration with Agent-Based Modeling: Combines deep reinforcement learning (DRL) with agent-based models (ABMs) to represent physical and regulatory rules governing cell movement.
• Utilization of 3D Time-Lapse Microscopy Data: Uses 3D time-lapse microscopy images to analyze cellular movements with high spatial and temporal resolution.
• Overcoming Local Optimization Challenges: Employs DRL exploration to mitigate local optimization issues of greedy, rule-based ABMs and to explore broader ranges of potential movement paths.
• Inference of Movement Mechanisms: Enables inference of mechanisms such as active intercalation and leader–follower collective migration from simulated migration paths.

Scientific Applications:

• Modeling specific developmental processes: Applied to the anterior intercalation movement of the Cpaaa cell and to rearrangement of superficial left-right asymmetry in C. elegans embryos.
• Insights into cell movement mechanisms: Infers that Cpaaa intercalation is an active, directional process influenced by distant cellular interactions and that left-right asymmetry rearrangement involves a leader–follower migration mechanism.
• Reverse engineering regulatory mechanisms: Simulates and analyzes potential migration paths to infer regulatory mechanisms governing cell movements during embryogenesis.

Methodology:

Applies deep reinforcement learning within an agent-based modeling framework using 3D time-lapse microscopy data, with DRL exploration employed to mitigate local optimization of greedy rule-based ABMs and to simulate and analyze potential migration paths.