Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis

TL;DR

This paper introduces a deep reinforcement learning approach integrated with agent-based modeling to accurately simulate and analyze complex cell movements during early C. elegans embryogenesis, surpassing traditional rule-based models.

Contribution

It presents a novel deep reinforcement learning framework within an agent-based model to better understand cell migration mechanisms in embryonic development.

Findings

Successfully modeled active directional cell movement of Cpaaa.

Revealed that cell intercalation is driven by active movement rather than passive neighbor effects.

Explained collective cell movement through a leader-follower mechanism.

Abstract

Cell movement in the early phase of C. elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have shown that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulation networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse images. We present a deep reinforcement learning approach within an ABM system to characterize cell movement in C. elegans embryogenesis. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, ABM that uses greedy algorithms. We tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the left-right asymmetry. In the first case, model results showed that Cpaaa's intercalation is an active directional cell movement caused by the continuous effects from a longer distance, as opposed to a passive movement caused by neighbor cell movements. This is because the learning-based simulation found that a passive movement model could not lead Cpaaa to the predefined destination. In the second case, a leader-follower mechanism well explained the collective cell movement pattern. These results showed that our approach to introduce deep reinforcement learning into ABM can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to explore large datasets generated by live imaging.