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至关重要，通常是通过劳动分裂来实现的，一些细胞在信息处理任务中充当“领导者”，而其他细胞则作为“追随者”。但是，在许多系统中，尚不清楚是否呈现领导者，还是出现在应对环境挑战的情况下。在紧急领导力的情况下，它不太了解异质性和细胞间耦合导致领导者出现，以及他们作为领导者的作用是否随着时间而学习。在这里，我们建议使用激动人心的动态，Hebbian学习和渗透理论的新型组合来研究新兴领导的现象，并使用对神经细胞的单层的自定义微流体实验来测试我们的预测。总体目标是获得对协调的，令人兴奋的系统的行为的一般理解，在该系统中，异质性和可塑性起着驾驶作用。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社区中领导者细胞（早期响应者）出现的机制，（（2）测试对重复刺激学习领导者/自行车员身份的竞争假设，以及（3）与活性和通信细胞的共同培养，以调查细胞的过度细胞来调查领域的信息，以调查领导者的信息。我们认为，正如数学建模可以帮助解释生物学数据一样，生物学实验也可以激发新的数学思想，只要两者通过定量测量和错误的预测耦合。由于在细胞生物学中都发现了多体刺激系统，因此我们希望我们的结果具有广泛的影响，尤其是在数学和生物医学科学的界面上。