Physics (Internal)

Nonlinearity and Stochasticity in Biochemical Networks

This event is part of the PhD Final Oral Exams.

Dissertation Committee: Pankaj Mehta, Kiril Korolev, Martin Schmaltz, Rama Bansil, Daniel Segre

Abstract: Unicellular organisms exhibit elaborate collective behaviors in response to environmental cues. These behaviors are controlled by complex biochemical networks within individual cells and coordinated through cell-to-cell communication. Describing these behaviors requires new mathematical models that can bridge scales -- from biochemical networks within individual cells to spatially structured cellular populations. Here, I present a multiscale model for the emergence of collective behavior in the social amoeba Dictyostelium discoideum. These cells normally live as individual organisms, but upon starvation, communicate through periodic secretion of the small signaling molecule cAMP. The proposed model exploits new experimental advances that allow for the direct measurement and manipulation of this molecule. Inspired by these experiments, I model the Dictyostelium signaling network as a noisy excitable system coupled to a pre-processing module. I use this model to study spatially unstructured populations by constructing phase diagrams that relate the properties of population-level oscillations to parameters in the underlying biochemical network. Then, I extend this model to include spatial structure and show how it naturally gives rise to spiral waves. On each scale of complexity, the behavior of the model is compared to experimental results. This modeling approach provides a powerful tool for bridging scales in modeling of Dictyostelium populations.