Controlling collective behavior in eukaryotic cell populations

Princeton University, Princeton NJ

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Abstract

DESCRIPTION (provided by applicant): The overall goal of this proposal is to understand how nonlinear spatio-temporal dynamics of single cells give rise to coherent behaviors at the multi-cell stage in the social amoeba Dictyostelium discoideum. We will achieve this through a combination of high-precision measurements and mathematical modeling. In this system, starved amoebae engage in a developmental program as an alternate survival strategy. Individual cells communicate via the signaling molecule cAMP, which serves as a cue for chemotaxis that leads cells to aggregate and form a multi-cellular slime mold. The specific goals of this proposal are (1) to obtain a quantitative description for single cell cAMP signaling, (2) to understand single cell gradient sensing and its relationship to cAMP signaling, and (3) to develop a multi-cell model that recapitulates observed collective behaviors in Dictyostelium cell populations. Developing these models will answer three fundamental questions: What are the essential degrees of freedom of individual cells that characterize the cell's cAMP signaling dynamics? How extra-cellular gradient sensing is linked to cytosolic cAMP levels? How can large-scale multi- cellular spatio-temporal signaling patterns and cellular aggregation be inferred from intra- and inter-cellular cAMP signaling dynamics? Answering these questions will expand our understanding of how molecular signaling and cellular interactions lead to collective multi-cellular behaviors, and ultimately guide us to find ways to control such behaviors. From a practical point of view, this proposal builds on a new set of methods we have invented that have enabled us to successfully monitor both intra- and extra-cellular concentrations of the signaling molecule cAMP in individual cells. Social amoebae provide a unique opportunity for experiment- driven quantitative modeling because they allow for measurements simultaneously at the single cell and at the multi-cell levels; cells can be confined into highly controllable microfluidic environments and numerous signaling and aggregation mutants are available from a genetic databank. From a broad perspective, the research is likely to yield new experimental and quantitative tools for analyzing cell-to-cell signaling and the single-to-multi-cell transition of novel emergent behaviors.