Collaborative Research: Characterizing Two Cell Polarity Processes Using Uncertainty Quantification to Analyze Complex Models and Data

协作研究：使用不确定性量化来分析复杂模型和数据来表征两种电池极性过程

• 批准号: 1812843
• 负责人: Tau-Mu Yi
• 金额: $ 17.88万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812843&HistoricalAwards=false
• 关键词: Collaborative; Research; Characterizing; Two; Cell

The goal of this project, jointly funded by the Division of Mathematical Sciences Mathematical Biology Program and Division of Molecular and Cellular Biosciences Cellular Dynamics and Function Program, is to obtain a more detailed understanding of how cells "polarize" to generate different shapes. Specifically, how do cells rearrange themselves to go from a symmetric object like a round egg cell into an asymmetric object like a nerve cell? Experiments will be performed on budding yeast which can switch from a sphere to an asymmetric projection shape by localizing components to one end, a process termed cell polarization. Examples of polarization include immune cell activation, tumor cell metastasis, and yeast infections in which yeast cells invade human tissue by polarized cell growth. Fungal infections are increasing in prevalence in the U.S. Insights can help researchers discover treatments to halt the invasive growth of yeast infections. The research will investigate the spatial distribution of cellular proteins during polarization using a combination of mathematical modeling and microscopy imaging. Experimental data can be compared to computer simulation data to confirm the models and adjust the model parameters. An important methodological advance will be replacing the model, which can take significant time to run, with a simpler polynomial surrogate model which can be calculated very quickly resulting in a dramatic computational speed-up. Through this process one obtains models that can reproduce and predict the behavior of polarizing cells. In addition, the research will be integrated with outreach activities training graduate, undergraduate, and high school students on how to perform quantitative microscopy experiments and simulate mathematical models.Cell polarity and morphology define the form and function of individual and groups of cells. A systems biology approach will delve into this subject at a more quantitative level moving beyond arrow diagrams to characterize the spatial dynamics of cell polarity. The investigation will take advantage of the experimental tractability of budding yeast to characterize two classic cell polarity morphologies: the bud and the mating projection. More specifically, the investigation will use microscopy to visualize the spatial dynamics of polarization proteins and process the images into quantitative data. In parallel, a collection of mathematical models of budding and mating projection growth will be constructed based on biological hypotheses. Methodologically, one of the grand challenges of systems biology is to estimate the models/parameters using large datasets. Bayesian inference will be used to select the best models and estimate the parameters based on the experimental data. A central concept in this proposal will be applying techniques from uncertainty quantification (UQ) that replace model evaluations in the Monte Carlo method with a surrogate polynomial function, resulting in a dramatic speed-up of the uncertainty analysis. The combined result will be a systematic investigation of cell polarity leading to model predictions that will be tested by experiments converting one cellular morphology into the other. In addition, the proposed study of improved surrogate model calculation will further accelerate the uncertainty analysis of complex models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目由数学科学数学生物学学部和分子与细胞生物科学学部细胞动力学与功能学部共同资助，目的是更详细地了解细胞是如何“极化”产生不同形状的。具体地说，细胞如何重新排列自己，从圆形卵细胞这样的对称对象变成神经细胞这样的不对称对象？实验将在萌芽酵母上进行，这种酵母可以通过将成分定位到一端，从球状转变为不对称的投影形状，这一过程被称为细胞极化。极化的例子包括免疫细胞激活、肿瘤细胞转移和酵母感染，在这些感染中，酵母细胞通过极化细胞生长侵入人体组织。真菌感染在美国的患病率正在上升。洞察力可以帮助研究人员找到阻止酵母菌感染侵袭性生长的方法。这项研究将使用数学建模和显微成像相结合的方法来研究极化过程中细胞蛋白质的空间分布。可以将实验数据与计算机模拟数据进行比较，以确定模型并调整模型参数。一项重要的方法进步将是用一种更简单的多项式替代模型取代运行起来可能需要很长时间的模型，该模型可以非常快速地计算，从而显著提高计算速度。通过这个过程，人们得到了可以复制和预测极化细胞行为的模型。此外，这项研究将与外展活动相结合，培训研究生、本科生和高中生如何进行定量显微镜实验和模拟数学模型。细胞的极性和形态定义了个体和细胞组的形式和功能。系统生物学方法将在更定量的水平上深入研究这一主题，超越箭头图来表征细胞极性的空间动力学。这项研究将利用发芽酵母的实验易操纵性来表征两种经典的细胞极性形态：芽状和交配突起。更具体地说，这项研究将使用显微镜来可视化极化蛋白质的空间动态，并将图像处理成定量数据。同时，将基于生物学假说建立一组萌芽和交配投影生长的数学模型。在方法论上，系统生物学的重大挑战之一是使用大数据集估计模型/参数。贝叶斯推理将被用来选择最佳模型，并根据实验数据估计参数。该提案的核心概念将是应用不确定度量化(UQ)技术，用替代多项式函数取代蒙特卡罗方法中的模型评估，从而大大加快不确定度分析的速度。合并后的结果将是对细胞极性的系统研究，从而得出模型预测，该预测将通过将一种细胞形态转换为另一种细胞形态的实验来检验。此外，拟议的改进代理模型计算研究将进一步加快复杂模型的不确定性分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。