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.
Recent research reveals that cellular collective behaviors emerging from cell-to-cell communication are both ubiquitous and essential for the organism's survival. When groups of individual cells cooperate, the behavior of the collective is not easily deduced from the behavior of the individuals. In some cases, collective interactions can be hijacked by malign phenomena such as cancer. Hence there is a crucial need to understand these collective behaviors. The ultimate goal is to reprogram collective behaviors in cellular populations. This approach has the potential to promote novel therapies by, for example, directly guiding immune responses via immune cells or targeting tumors to prevent them from spreading.
|Gregor, Thomas (2017) Beyond D'Arcy Thompson: Future challenges for quantitative biology. Mech Dev 145:10-12|
|Sgro, Allyson E; Schwab, David J; Noorbakhsh, Javad et al. (2015) From intracellular signaling to population oscillations: bridging size- and time-scales in collective behavior. Mol Syst Biol 11:779|
|Tarnita, Corina E; Washburne, Alex; Martinez-Garcia, Ricardo et al. (2015) Fitness tradeoffs between spores and nonaggregating cells can explain the coexistence of diverse genotypes in cellular slime molds. Proc Natl Acad Sci U S A 112:2776-81|
|Noorbakhsh, Javad; Schwab, David J; Sgro, Allyson E et al. (2015) Modeling oscillations and spiral waves in Dictyostelium populations. Phys Rev E Stat Nonlin Soft Matter Phys 91:062711|
|Little, Shawn C; Gregor, Thomas (2013) Sorting sloppy Sonic. Cell 153:509-10|
|Gregor, Thomas; Fujimoto, Koichi; Masaki, Noritaka et al. (2010) The onset of collective behavior in social amoebae. Science 328:1021-5|
|Mehta, Pankaj; Gregor, Thomas (2010) Approaching the molecular origins of collective dynamics in oscillating cell populations. Curr Opin Genet Dev 20:574-80|