权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
1813071
负责人：
Ching-Shan Chou
金额：
$ 22万
依托单位：
Ohio State University
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 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813071&HistoricalAwards=false
关键词：
Collaborative Research Characterizing Two Cell

项目摘要

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.

该项目由数学科学部数学生物学项目和分子与细胞生物科学部细胞动力学与功能项目共同资助，旨在更详细地了解细胞如何“极化”以产生不同的形状。具体来说，细胞是如何重新排列自己，从一个对称的物体，如圆形卵细胞，变成一个不对称的物体，如神经细胞？实验将在出芽酵母上进行，酵母可以通过将成分定位到一端，从球体转变为不对称的凸出形状，这一过程称为细胞极化。极化的例子包括免疫细胞激活、肿瘤细胞转移和酵母感染，其中酵母细胞通过极化细胞生长侵入人体组织。真菌感染在美国的流行率正在上升。洞察可以帮助研究人员发现阻止酵母菌感染侵袭性生长的治疗方法。该研究将利用数学建模和显微镜成像相结合的方法来研究细胞蛋白质在极化过程中的空间分布。实验数据可以与计算机仿真数据进行比较，以确定模型和调整模型参数。一个重要的方法进步将是用一个更简单的多项式代理模型取代模型，该模型可以非常快速地计算，从而大大提高计算速度。通过这个过程，我们得到了可以复制和预测极化细胞行为的模型。此外，该研究将与拓展活动相结合，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。细胞的极性和形态决定了细胞个体和细胞群的形态和功能。系统生物学方法将在更定量的水平上深入研究这个问题，超越箭头图来表征细胞极性的空间动态。该研究将利用芽殖酵母的实验可追溯性来表征两种经典的细胞极性形态：芽和交配投影。更具体地说，这项研究将使用显微镜来可视化极化蛋白的空间动态，并将图像处理成定量数据。同时，将根据生物学假设建立一系列出芽和交配投影生长的数学模型。在方法上，系统生物学的一个重大挑战是使用大数据集估计模型/参数。基于实验数据，采用贝叶斯推理选择最佳模型并估计参数。本提案的核心概念是应用不确定性量化（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性分析的速度。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，并将通过将一种细胞形态转换为另一种细胞形态的实验进行测试。此外，提出的改进代理模型计算的研究将进一步加快复杂模型的不确定性分析。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。