Excitability in Dictyostelium development

Discovering how populations of cells reliably develop into complex multi-cellular structures is a key challenge in modern developmental biology. This requires an understanding of how networks at the single-cell level, when combined with intercellular signaling and environmental cues, give rise to the collective behaviors observed in cellular populations. I will present work in collaboration with the Gregor lab, showing that the signal-relay response of starved cells of the amoebae Dictyostelium discoideum can be well modeled as an excitable system. This is in contrast to existing models of the network that postulate a feed-forward cascade. I then extend the signal-relay model to describe how spatial gradient sensing may be achieved via excitability. One potential advantage of relying on feedback for gradient sensing is in preventing ``cheaters'' that do not produce signals from taking over the population. I then combine these models of single-cell signaling and chemotaxis to perform large-scale agent-based simulations of aggregating populations. This allows direct study of how variations in single-cell dynamics modify population behavior. In order to further test this model, I use the results of a screen for mutant cell lines that exhibit altered collective patterns. Finally, I use an existing FRET movie database of starved cell populations at varying cell densities and dilution rates to study heterogeneity in repeated spatio-temporal activity patterns.