Collaborative Research: Spatiotemporal Learning in Communicating Cell Populations

合作研究：交流细胞群的时空学习

• 批准号: 10269047
• 负责人: Andrew Mugler
• 金额: $ 25.09万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-24 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10269047
• 关键词: Cells; Instruction; Learning; Population; Research; spatiotemporal

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.

多细胞协调在生物学中是必不可少的，通常通过劳动分工来实现，在信息处理任务中，一些细胞充当“领导者”，另一些细胞充当“追随者”。然而，在许多系统中，并不清楚领导者是预先选定的，还是为了应对环境挑战而出现的。在领导力涌现的案例中，人们对异质性和细胞间耦合如何导致领导者涌现以及他们作为领导者的角色是否是随着时间的推移而习得的知之甚少。在这里，我们建议调查的现象，紧急的领导使用一种新的组合，兴奋的动力学，赫布学习和渗流理论，并测试我们的预测使用自定义的微流体实验单层神经细胞。总体目标是获得一个协调的，可兴奋的系统，其中异质性和可塑性起着驱动作用的行为的一般理解。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细胞（早期反应者）在社区中出现的机制，（2）测试重复刺激下学习领导者/追随者身份的竞争假设，以及（3）与过度活跃和沟通缺陷的细胞共培养，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结合起来。由于多体可兴奋系统在细胞生物学中被发现，我们希望我们的结果具有广泛的影响，特别是在数学和生物医学科学的界面上。