Stochastic 3D Simulations of Polarization and Growth of the Yeast Mating Projection on Static and Moving Boundary Domains

Brian Drawert
Department of Computer Science
University of California Santa Barbara
Santa Barbara, California, USA


Polarization is an essential behavior of living cells, yet the dynamics of this symmetry- breaking process are not fully understood. In previous work we developed a spatial stochastic model of cellular polarization during mating of Saccharomyces cerevisiae. Specifically we investigated the ability of yeast cells to sense a spatial gradient of mating pheromone and respond by forming a projection in the direction of the mating partner. Our results demonstrated that a spatial stochastic model of polarisome formation can more robustly reproduce two fundamental characteristics observed in wild-type cells: a tightly polarized phenotype and the ability to track moving pheromone input, in comparison with the corresponding deterministic model.

Existing models of cell polarization have focused solely on the biochemical signaling system. However, there exists a well-known interplay between the growth of the mating projection and the mechanical forces of the cell wall in determining the shape of the cell. The cell wall of S. cerevisiae both defines its shape and provides the mechanical integrity necessary to sustain the large internal turgor pressure. Under the isotropic push of turgor pressure, polarized expansion occurs via localized assembly of new cell wall material in combination with a simultaneous softening of the cell wall, inducing it to yield and locally expand. Intracellular signaling directs enzymes with the ability to modify cross-linking of polymers in the cell wall to the region of polarization. The resulting mechanical feedback from the wall expansion initiates the delivery of raw material via vesicular transport.

To accurately model this complex biological phenomena, we have developed a multi- scale computational framework for simulating the coupling of the stochastic dynamics of biochemical reactions involved in shaping walled cells to the mechanical processes of cell wall expansion and growth. Our computational method exploits the time-scale separation between the relatively slow dynamics of the cell wall and the rapid interactions of the intercellular signaling network. This method is a critical tool for furthering our quantitative understanding of how cells establish and control their shapes, and our ability to predict morphological phenotype from the underlying genotype.