Collaborative Research: Next-Generation Algorithms for Stochastic Spatial Simulation of Cell Polarization

细胞极性是真核细胞多样性功能的重要组成部分，以前均匀分布的细胞成分变得不对称。 细胞极性的一个标志是空间定位。 从建模的角度来看，空间定位不能理解没有适当的空间动力学模型控制其创建和时间演变。 与此同时，空间动态受到表现为细胞噪声的随机事件的深刻影响。因此，深入了解细胞极性不可避免地需要适当的建模，模拟和随机空间动态分析。 在过去，限制这一领域工作的根本问题是随机空间模拟的计算复杂性。 本计画发展实验数据与演算法，以模拟、分析酵母信息素反应系统中细胞极性所产生的空间随机动力学。 针对空间随机模拟中计算量大的问题，提出了一种新的算法。 然后，该算法被进一步开发，然后集成到一个强大的软件基础设施，使其广泛使用。 能够捕捉随机变化的实验为模型开发和分析提供信息。作为该项目的结果开发的软件可以在科学和工程领域对高度复杂的空间随机现象进行常规模拟。 所有软件都将广泛提供。 将在会议和讲习班上提供培训课程和介绍，以确保研究的可获得性。 参与该项目的研究生提供了一个独特的，高度多学科的研究经验。 UCSB的学生在Petzold和Khammash的共同指导下组成了一个紧密的团队，并延长了对UCI的访问，在Yi的实验室工作，了解实验技术的可能性和局限性。 专注于实验的UCI学生在UCSB花费大量时间与建模人员一起工作，第一手了解系统级方法可以为生物研究带来什么。

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.