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Collaborative Research: ABI Innovation: Automated Prioritization and Design of Experiments to Validate and Improve Mathematical Models of Molecular Regulatory Systems

合作研究：ABI 创新：自动优先排序和实验设计，以验证和改进分子调控系统的数学模型

基本信息

批准号：
1759858
负责人：
Th Murali
金额：
$ 115.58万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759858&HistoricalAwards=false
关键词：
Collaborative Research ABI Innovation Automated

项目摘要

Complex networks of interacting molecules control all the physiological processes that occur in a living cell. It is impossible to deduce the functions of these networks using intuitive reasoning alone. Therefore, scientists construct mathematical models of cellular processes that can be simulated in the computer. Unfortunately, it takes many years of careful study of the scientific literature and steady, incremental progress to construct detailed, comprehensive, and accurate mathematical models. This project will create an integrated computational - experimental framework that will significantly accelerate the process of mathematical modeling. The project will create several scientific innovations including (a) novel approaches to searching the space of model simulations to identify promising predictions, (b) computational techniques to efficiently plan experiments, (c) experimental methods that use these plans to rapidly test model predictions, and (d) automatic techniques to extend and refine the models to accommodate the results of these experiments. The project will benefit science by applying this framework to develop a comprehensive, new model that describes how nutrients control the growth of baker's yeast cells. Long term benefits to society will accrue from the use of the methods developed by this project to study any complex cellular system, e.g., those implicated in cell proliferation in cancers, wound healing, and tissue regeneration. Computational cell biologists have constructed detailed, mechanistic, and predictive mathematical models of many physiological processes in living cells. In principle, such models can predict the phenotypes of novel combinations of gene mutations. However, this potential has not been fully realized for three reasons: (a) the number of possible combinations grows explosively, complicating the search and prioritization of informative mutants, (b) it is impossible to manually plan experiments to make and characterize thousands of mutants, and (c) automated techniques that can resolve contradictions between experimental results and model predictions are still under development. The goal of this project is to create a unique, integrated framework that will address these challenges by (a) systematically generating informative predictions from mathematical models, (b) computationally synthesizing high-throughput experimental plans to test these predictions, and (c) rapidly reconciling inconsistencies between model and experiment. The project will apply this framework to models of cell growth and division in budding yeast. This transformative approach will streamline and accelerate the mathematical modeling cycle. The computational approaches developed for synthesizing experimental plans will be broadly applicable to other organisms, including mammalian cells, that can be systematically perturbed using siRNA or CRISPR/Cas9. Because nutrient conditions, metabolic fluxes, energy budgets, protein synthesis, and cell cycle regulation are central to wound healing and tissue regeneration, to the engineering of artificial tissues and organs, and to the expansion and spread of tumors, the methods and models we develop here in the context of budding yeast cell biology will be of great relevance to mammalian biology. The educational component of our project will infuse computational thinking into biology at the undergraduate level and encourage students with backgrounds in life science, engineering, or computation to consider systems biology as a career choice. The project will offer a 10-week summer research institute on 'Computationally - Driven Experimental Biology' to six undergraduate students, consisting of lectures on project -related topics and a single collaborative research project. Involving all the students in a single research project will expose them to team science and give them an appreciation of how computer science, mathematics, and experimental cell biology can be seamlessly interwoven to study cellular processes. The results of this project will appear at http://bioinformatics.cs.vt.edu/~murali/research.html .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.

相互作用分子的复杂网络控制着活细胞中发生的所有生理过程。仅凭直觉推理是不可能推断出这些网络的功能的。因此，科学家们构建了可以在计算机中模拟的细胞过程的数学模型。不幸的是，这需要多年的科学文献和稳定的仔细研究，渐进的进展，以构建详细，全面和准确的数学模型。该项目将创建一个集成的计算-实验框架，这将大大加快数学建模的过程。该项目将创造几项科学创新，包括（a）搜索模型模拟空间以确定有希望的预测的新方法，（B）有效规划实验的计算技术，（c）使用这些计划快速测试模型预测的实验方法，以及（d）扩展和改进模型以适应这些实验结果的自动技术。该项目将通过应用这一框架来开发一个全面的新模型，描述营养物质如何控制面包酵母细胞的生长，从而使科学受益。使用本项目开发的方法研究任何复杂的细胞系统，将为社会带来长期利益，例如，那些与癌症中的细胞增殖、伤口愈合和组织再生有关。计算细胞生物学家已经为活细胞中的许多生理过程构建了详细的、机械的和预测性的数学模型。原则上，这种模型可以预测基因突变的新组合的表型。然而，由于三个原因，这种潜力尚未完全实现：（a）可能的组合数量爆炸性增长，使信息突变体的搜索和优先级排序变得复杂，（B）不可能手动计划实验来制造和表征数千个突变体，以及（c）可以解决实验结果和模型预测之间矛盾的自动化技术仍在开发中。该项目的目标是创建一个独特的综合框架，通过以下方式应对这些挑战：（a）从数学模型中系统地生成信息预测，（B）计算合成高通量实验计划以测试这些预测，以及（c）快速协调模型和实验之间的不一致。该项目将把这一框架应用于芽殖酵母的细胞生长和分裂模型。这种变革性的方法将简化和加速数学建模周期。为合成实验计划而开发的计算方法将广泛适用于其他生物，包括哺乳动物细胞，这些生物可以使用siRNA或CRISPR/Cas9进行系统性干扰。由于营养条件，代谢通量，能量预算，蛋白质合成和细胞周期调控是伤口愈合和组织再生，人工组织和器官的工程，以及肿瘤的扩张和扩散的核心，我们在这里开发的方法和模型在芽殖酵母细胞生物学的背景下将与哺乳动物生物学有很大的相关性。我们项目的教育部分将在本科阶段将计算思维注入生物学，并鼓励具有生命科学，工程或计算背景的学生将系统生物学视为职业选择。该项目将为六名本科生提供为期10周的“计算驱动的实验生物学”暑期研究所，包括与项目相关主题的讲座和一个单一的合作研究项目。让所有学生参与一个单一的研究项目将使他们接触到团队科学，并让他们了解计算机科学，数学和实验细胞生物学如何无缝交织在一起来研究细胞过程。该项目的结果将出现在http://bioinformatics.cs.vt.edu/~murali/research.html上。该奖项反映了NSF的法定使命，并被认为值得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来支持。