Integrative Graduate
Education and
Research Traineeship

Highlight

Stochastic Modeling and Simulation of Cell Polarization During Mating in Budding Yeast

Polarisome Fragmentation and Scanning

Projects:

Graduate Education Program in Computational Science and...

Institution:

University of California at Santa Barbara

Primary Strategic Goal:

Discovery: Foster research that will advance the frontiers of knowledge, emphasizing areas of greatest opportunity and potential benefit and establishing the Nation as a global leader in fundamental transformational science and engineering.

Secondary Strategic Goal:

Research Infrastructure: Build the Nation's research capability through critical investments in advanced instrumentation, facilities, cyber infrastructure, and experimental tools.

Achievement/Results

One of the best-studied examples of cell polarization is the growth of the mating projection during yeast mating. Yeast cells localize specific proteins to the front of the cell in response to a spatial gradient of mating pheromone secreted by the partner. The spatial sensing and response exhibit remarkable sensitivity, dynamic range, and robustness. A single molecular entity located at the front of the cell, termed the polarisome, helps to organize structural, transport and signaling proteins. Prior work has produced deterministic (PDE) mathematical models that described the spatial dynamics of yeast cell polarization in response to spatial gradients of mating pheromone, as well as addressing the trade-off between amplification and tracking. A goal of our spatial stochastic model is to explore the possibility that noise in the system allows non-leading edge sites to temporarily cross the activation threshold.

Address Goals

Current methods for Spatial Stochastic Simulation are still extremely expensive and typically involve substantial restrictions on the time step. We have developed a computational method to efficiently simulate the large number of diffusion transfer events that occur in the stochastic simulation of spatially inhomogeneous systems. The new hybrid algorithm uses the Finite State Projection to calculate a large number of diffusive events at once, allowing large simulation time steps and providing a bound on the error. We show how the special structure of the diffusion operator can be exploited to improve the efficiency of the Finite State Projection method. The Finite State Projection method is combined with Spatial Stochastic Algorithm simulation of the reaction channels to create an efficient and accurate method for simulation of reaction-diffusion systems. Work is under way to integrate the new method into an easily accessible software framework to enable its use by the wider scientific community.