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

细胞极性，即以前均匀分布的细胞成分变得不对称定位的细胞极性，对真核细胞的各种特化功能至关重要。细胞极性的一个标志是空间定位。从建模的角度来看，如果没有对控制其产生和时间演变的空间动力学进行适当的建模，就无法理解空间定位。与此同时，空间动力学受到表现为细胞噪声的随机事件的深刻影响。因此，要深入了解细胞极性，不可避免地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。本项目为酵母信息素响应系统中细胞极性产生的空间随机动力学建模、模拟和分析提供实验数据和算法。为了解决空间随机模拟中计算量大的问题，提出了一种新的算法。然后进一步开发该算法，然后将其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信息。该项目开发的软件使科学和工程领域高度复杂的空间随机现象的常规模拟成为可能。所有的软件都将被广泛使用。将提供辅导课和在会议和讲习班上的演讲，以确保研究的可及性。参与该项目的研究生将获得独特的、高度跨学科的研究经验。在Petzold和khamash的共同指导下，UCSB的学生组成了一个紧密结合的团队，他们经常访问UCI，在Yi的实验实验室工作，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者一起工作，第一手了解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.