Collaborative Research: Spatiotemporal Learning in Communicating Cell Populations

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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.