Cell polarity, whereby cellular components that were previously uniformly distributed become asymmetrically localized, is essential to the diverse specialized functions of eukaryotic cells. A hallmark of cell polarity is spatial localization. From a modeling point of view, spatial localization cannot be understood without proper modeling of the spatial dynamics governing its creation and time evolution. At the same time, spatial dynamics are profoundly influenced by stochastic events that manifest as cellular noise. Therefore, deep understanding of cell polarity inevitably requires the proper modeling, simulation, and analysis of stochastic spatial dynamics. The fundamental problem limiting work in this area in the past has been the computational complexity of stochastic spatial simulations. This project develops the experimental data and algorithms for modeling, simulation and analysis of spatial stochastic dynamics arising in cell polarity in the yeast pheromone response system. A novel algorithm is developed to address the computationally intensive task of spatial stochastic simulation. The algorithm is then further developed and then integrated into a powerful software infrastructure to enable its widespread use. Experiments capable of capturing stochastic variability inform model development and analysis.
Software developed as a result of this project enables routine simulation of highly complex spatial stochastic phenomena across the sciences and engineering. All software will be made widely available. Tutorial courses and presentations at meetings and workshops will be given to ensure the accessibility of the research. Graduate students involved in this project are provided with a unique, highly multidisciplinary research experience. Students at UCSB work as a tightly-knit team co-advised by Petzold and Khammash, with extended visits to UCI to work in Yi's experimental lab, learning about the possibilities and limitations of the experimental techniques. UCI students focused on experiment spend significant time at UCSB working with the modelers, learning first-hand what the systems-level approach can bring to biological research.
