CAREER: Stochastic Analyses to Optimize Designs for Single-Cell Optical Microscopy Experiments

职业：通过随机分析优化单细胞光学显微镜实验的设计

• 批准号: 1941870
• 负责人: Brian Munsky
• 金额: $ 80.97万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941870&HistoricalAwards=false
• 关键词: CAREER; Stochastic; Analyses; Optimize; Designs

Computational analyses can provide powerful keys to unravel basic biological mysteries, to discover new mechanisms, and to drive national advances in medical, agricultural, and environmental sciences. The exceptionally complicated nature of biological processes requires that new computational tools be created to address specific challenges and opportunities that frequently arise in modern biological investigations. This project discovers, improves, and validates several sets of integrated computational and experimental methods to explore and more fully understand one of the most fundamental processes in molecular biology – the translation of individual mRNA molecules to form functional proteins. The project creates and distributes new computational software packages to design, interpret, and predict cutting-edge single-cell and single-molecule experiments for use in future biological and biomedical investigations. This project also incorporates a new three-week annual summer school program to train undergraduate students from the physical, engineering, and computer sciences, to use modern statistical methods to measure, analyze, predict, and control complex biological processes. In addition, the project involves developing and disseminating an online quantitative biology course curriculum for senior undergraduate and graduate students to complement the recent community-written textbook Quantitative Biology: Theory, Computational Methods, and Models (MIT Press, 2018).This project creates, tests, and validates new technologies to integrate super-resolution fluorescence microscopy experiments with high-performance computing to measure, interpret and predict the complex dynamics of single-mRNA translation within variable genomic and environmental contexts. Project goals comprise three integrated research objectives: (1) Develop rigorous computational tools to quantify and take advantage of intrinsic and extrinsic fluctuations (often called ‘process noise’) that affect the dynamics of single-mRNA translation, while minimizing impacts of experimental measurement noise; (2) Demonstrate new methodologies to rigorously estimate model uncertainties and choose the best possible experiments to resolve subtle differences in competing hypotheses in the most efficient and cost-effective manner; and (3) Combine intrinsic, temporal mRNA translation fluctuations with statistical analyses to enable simultaneous measurement of multiple translating mRNA species in real time and in single, living cells. The project incorporates tools from all three objectives to produce and distribute an open-source, user-friendly Python software package called RNA Sequence to NAscent Protein Simulator (rSNAPSim), which enables experimental biologists to analyze, predict, and redesign single-mRNA translation experiments.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.

计算分析可以提供强大的钥匙来解开基本的生物奥秘，发现新的机制，并推动国家在医学、农业和环境科学方面的进步。生物过程异常复杂的性质要求新的计算工具被创建，以解决在现代生物学研究中经常出现的具体挑战和机遇。本项目发现、改进并验证了几套集成的计算和实验方法，以探索和更全面地理解分子生物学中最基本的过程之一-单个mRNA分子的翻译形成功能蛋白。该项目创建并分发新的计算软件包，用于设计、解释和预测未来生物和生物医学研究中使用的尖端单细胞和单分子实验。该项目还纳入了一个新的为期三周的年度暑期学校项目，以培训物理、工程和计算机科学专业的本科生使用现代统计方法来测量、分析、预测和控制复杂的生物过程。此外，该项目还涉及为高年级本科生和研究生开发和传播在线定量生物学课程，以补充最近社区编写的教科书定量生物学：理论，计算方法和模型（麻省理工学院出版社，2018）。该项目创建、测试和验证了将超分辨率荧光显微镜实验与高性能计算相结合的新技术，以测量、解释和预测可变基因组和环境背景下单mrna翻译的复杂动态。项目目标包括三个综合研究目标：(1)开发严格的计算工具来量化和利用影响单mrna翻译动力学的内在和外在波动（通常称为“过程噪声”），同时最大限度地减少实验测量噪声的影响；(2)展示新的方法来严格估计模型的不确定性，并选择最好的实验，以最有效和最具成本效益的方式解决竞争假设中的细微差异；(3)将内在的、时间的mRNA翻译波动与统计分析相结合，从而能够在单个活细胞中实时同时测量多个翻译mRNA物种。该项目结合了所有三个目标的工具，以生产和分发一个开源的、用户友好的Python软件包，称为RNA序列到新生蛋白质模拟器（rSNAPSim），它使实验生物学家能够分析、预测和重新设计单mrna翻译实验。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。