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Simulating Yeast Polarization in the Cloud

From Q-bio

We have developed a discrete spatial stochastic model of cellular polarization during the mating of Saccharomyces cerevisiae. Specifically we investigate 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 mechanistic model integrates three components of the polarization process: the G-protein cycle activated by pheromone bound receptors, the focusing of a Cdc42 polarization cap, and the formation of the tight localization of proteins on the membrane known as the polarisome. Our results demonstrate that higher levels of stochastic noise result in increased robustness, giving support to a cellular model where noise and spatial heterogeneity combine to achieve robust biological function. This also highlights the importance of spatial stochastic modeling to reproduce experimental observations. Lastly we demonstrate how these models were built, executed and analyzed via MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models that we have developed. MOLNs is based on IPython and provides an interactive programming platform for the development of shareable and reproducible distributed parallel computational experiments.