Authors: Zi Wang,Dali Wang,Chengcheng Li,Yichi Xu,Husheng Li,Zhirong Bao
ArXiv: 1801.04600
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Abstract URL: http://arxiv.org/abs/1801.04600v2
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.