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

细胞极性对真核细胞的各种特殊功能是必不可少的，细胞成分以前是均匀分布的，现在变成了不对称的局部化。细胞极性的一个标志是空间定位。从建模的角度来看，如果不对支配空间局部化产生和时间演变的空间动力学进行适当的建模，就无法理解空间局部化。与此同时，空间动力学受到随机事件的深刻影响，这些随机事件表现为细胞噪音。因此，深入理解细胞的极性必然需要对随机空间动力学进行适当的建模、模拟和分析。过去限制这一领域工作的基本问题是随机空间模拟的计算复杂性。该项目开发了酵母信息素响应系统中细胞极性产生的空间随机动力学的建模、模拟和分析的实验数据和算法。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中，以使其能够广泛使用。能够捕捉随机变化的实验为模型的开发和分析提供了信息。作为该项目的结果开发的软件使跨科学和工程的高度复杂的空间随机现象的常规模拟成为可能。所有软件都将被广泛使用。将举办辅导课程，并在会议和讲习班上作陈述，以确保研究的可及性。参与该项目的研究生将获得独特的、高度多学科的研究体验。UCSB的学生在Petzold和Khammash的共同建议下，作为一个紧密的团队工作，长期访问UCI，在易的实验实验室工作，了解实验技术的可能性和局限性。UCI专注于实验的学生在UCSB花费大量时间与建模人员合作，第一手学习系统级方法可以为生物学研究带来什么。

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.