C.早期细胞运动的深度强化学习。线虫胚胎发生
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis
摘要：
细胞运动在C.秀丽线虫的发育是由一个高度复杂的过程来调节的，在这个过程中，一套规则和联系在不同的尺度上形成。以往的研究表明，基于主体的多尺度建模系统可以整合物理和生物规则，为研究发育系统提供新的途径。然而，应用这些系统来模拟细胞运动仍然具有挑战性，需要在正确的尺度上全面了解调控网络。深度学习和强化学习的最新发展为使用3D延时图像探索细胞运动提供了前所未有的机会。我们提出了一种在ABM系统中的深度强化学习方法来表征C中的细胞运动。线虫胚胎发生我们的建模系统捕捉胚胎中细胞运动模式的复杂性，并克服了传统的基于规则的ABM，使用贪婪算法遇到的局部优化问题。我们用两个真实的发育过程测试了我们的模型：通过插入的Cpaaa细胞的前向运动和左右不对称的重排。在第一种情况下，模型结果表明，Cpaaa的嵌入是一个积极的定向细胞运动所造成的连续的影响，从一个较长的距离，而不是被动的运动所造成的邻居细胞的运动。这是因为基于学习的模拟发现，被动运动模型无法将Cpaaa引导到预定义的目的地。在第二种情况下，领导者-追随者机制很好地解释了集体细胞运动模式。这些结果表明，我们将深度强化学习引入ABM的方法可以通过从逆向工程的角度探索细胞迁移路径来测试调控机制。该模型为探索由实时成像生成的大型数据集打开了新的大门。
英文摘要：
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.
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