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

• 批准号: 1759900
• 负责人: Jean Peccoud
• 金额: $ 36.11万
• 依托单位: Colorado 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 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759900&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Automated

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.

相互作用的分子组成的复杂网络控制着活细胞中发生的所有生理过程。仅凭直觉推理不可能推断出这些网络的功能。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，要构建详细、全面和准确的数学模型，需要对科学文献进行多年的仔细研究，并稳步、渐进地进行。该项目将创建一个综合的计算-实验框架，大大加快数学建模的进程。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)有效计划实验的计算技术，(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种全面的新模型，描述营养如何控制面包师酵母细胞的生长，从而使科学受益。使用该项目开发的方法研究任何复杂的细胞系统，例如与癌症中的细胞增殖、伤口愈合和组织再生有关的方法，将为社会带来长期利益。计算细胞生物学家已经构建了关于活细胞中许多生理过程的详细的、机械的和可预测的数学模型。原则上，这样的模型可以预测新型基因突变组合的表型。然而，这一潜力尚未完全实现，原因有三：(A)可能的组合数量爆炸性增长，使信息丰富的突变体的搜索和优先排序复杂化；(B)不可能手动计划实验以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的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)系统地从数学模型生成信息性预测，(B)通过计算合成高通量实验计划以测试这些预测，以及(C)迅速调和模型和实验之间的不一致。该项目将把这个框架应用到萌芽酵母的细胞生长和分裂模型中。这种变革性的方法将简化和加快数学建模周期。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进行系统干扰。由于营养条件、代谢流量、能量平衡、蛋白质合成和细胞周期调节对伤口愈合和组织再生、人工组织和器官工程以及肿瘤的扩展和扩散至关重要，因此我们在萌芽酵母细胞生物学的背景下开发的方法和模型将与哺乳动物生物学密切相关。我们项目的教育部分将把计算思维注入到本科水平的生物学中，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作为职业选择。该项目将为6名本科生提供为期10周的暑期计算驱动实验生物学研究机构，包括与项目相关的讲座和一个单独的合作研究项目。让所有学生参与一个研究项目将使他们接触到团队科学，并让他们欣赏计算机科学、数学和实验细胞生物学如何无缝地交织在一起来研究细胞过程。该项目的结果将在http://bioinformatics.cs.vt.edu/~murali/research.html上公布。这一奖项反映了国家科学基金会的法定使命，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。

项目成果

Yeast genetic interaction screens in the age of CRISPR/Cas.

• DOI: 10.1007/s00294-018-0887-8
• 发表时间: 2019-04
• 期刊: Current genetics
• 影响因子: 2.5
• 作者: Adames NR;Gallegos JE;Peccoud J
• 通讯作者: Peccoud J