Collaborative Research: Spatiotemporal Learning in Communicating Cell Populations

多细胞协调在生物学中是必不可少的，通常是通过分工来实现的，在信息处理任务中，一些细胞充当“领导者”，另一些细胞充当“跟随者”。然而，在许多系统中，尚不清楚领导人是预先选出的，还是他们是为了应对环境挑战而出现的。在新兴领导力的案例中，人们很少了解异质性和细胞到细胞的耦合是如何导致领导者出现的，以及他们作为领导者的角色是否随着时间的推移而学习。在这里，我们建议使用可兴奋动力学、Hebbian学习和渗流理论的新组合来研究新兴领导力现象，并使用神经细胞单层的定制微流体实验来验证我们的预测。首要目标是对协调的、可兴奋的系统的行为有一个一般性的理解，在这些系统中，异质性和可塑性起着推动作用。我们将通过三个目标来实现这一目标：(1)利用我们的数学模型和实验来确定领导者细胞(早期反应者)在社区中出现的机制，(2)测试相互竞争的假设，以在重复刺激下学习领导者/追随者的身份，以及(3)产生与过度活跃和沟通不足的细胞的共培养，以研究领导者驱动的信息传递。我们认为，正如数学建模可以帮助解释生物数据一样，生物实验也可以激发新的数学思想，只要两者通过定量测量和可证伪的预测相结合。由于在细胞生物学中发现了多体可兴奋系统，我们预计我们的结果将具有广泛的意义，特别是在数学和生物医学科学的界面上。

Multicellular coordination is essential in biology and is often achieved by division of labor, with some cells acting as “leaders” and others as “followers” in an information-processing task. However, in many systems it is unclear whether leaders are preselected, or whether they instead emerge in response to an environmental challenge. In the case of emergent leadership, it is poorly understood how heterogeneity and cell-to-cell coupling cause leaders to emerge, and whether their role as leaders is learned over time. Here we propose to investigate the phenomenon of emergent leadership using a novel combination of excitable dynamics, Hebbian learning, and percolation theory, and to test our predictions using custom microfluidic experiments on monolayers of neural cells. The overarching goal is to obtain a generic understanding of the behavior of coordinated, excitable systems in which heterogeneity and plasticity play a driving role. We will achieve this goal via three aims: (1) utilize our mathematical model and experiments to determine the mechanism by which leader cells (early responders) emerge in the community, (2) test competing hypotheses for the learning of leader/follower identity upon repeated stimulation, and (3) generate co-cultures with hyperactive and communication-deficient cells to investigate leader-driven information transfer. We take the view that, just as mathematical modeling can help explain biological data, biological experiments can also inspire new mathematical ideas, so long as the two are coupled via quantitative measurements and falsifiable predictions. Because many-body excitable systems are found across cell biology, we expect our results to have broad implications, particularly at the interface of the mathematical and biomedical sciences.