NRT-URoL: Data Driven Biology

NRT-URoL：数据驱动生物学

• 批准号: 2125644
• 负责人: Beth Pruitt
• 金额: $ 300万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125644&HistoricalAwards=false
• 关键词: NRT; URoL; Data; Driven; Biology

This National Science Foundation Research Traineeship (NRT) award to the University of California, Santa Barbara will develop, implement, and test an innovative graduate education model on the theme of Data Driven Biology (DDB). The program's goal is to train a new generation of biological scientists and engineers who can work across disciplines, are fluent in data analytics and experimental methods, and who will advance fundamental research in quantitative biology and bioengineering. Cell and developmental biology are in transition from qualitative observational sciences to quantitative, data-rich fields that leverage modeling and design principles from physics and engineering. Advances in imaging and sequencing technologies, paired with machine learning and computer vision tools, are having a transformative impact on quantitative cell biology. To take full advantage of these technologies, the modern biological engineer needs to be fluent both in the design of biological experiments and in data mining strategies to integrate information across scales (temporal and from genetic/molecular to cellular and tissue scales). DDB seeks to provide students with the breadth to collaborate across disciplines meaningfully and with the depth to answer biological questions with scientific rigor supported by knowledge of and experience with data science approaches. The project anticipates training 70 Ph.D. students, including 30 funded trainees, from doctoral programs in: biological engineering, biomolecular science & engineering, chemical engineering, computer science, electrical and computer engineering, molecular, cellular and developmental biology, mechanical engineering, and physics. Through DDB, students will learn how to design experiments; acquire and integrate multi-modal, disparate data; and integrate machine learning and computational approaches to extract patterns and meaning from biological data to understand and leverage heterogeneity in stem-cell-derived models. Trainees will be supported by a new curriculum, which will serve as the basis of an emergent Biological Engineering Ph.D. program. Onboarding will include a structured course on seminal research papers and best practices for designing interdisciplinary inquiry. Armed with these training elements, students will be immersed in an in-vivo research experience to conduct hands-on redesign of seminal experiments and to personally implement advanced research methods to test the conclusions of these seminal papers. Students will also engage in co-mentored research rotation projects across diverse labs (experimental and modeling). To support self-reflection, deliberate career planning, and self-efficacy, the program will deploy a three-pronged mentoring plan, including a faculty advisor, peer feedback, and self-assessment through individual development plans. Finally, internships and externships will provide trainees with immersive exchange opportunities across a research network committed to convergent and translational training. This will allow students to experience firsthand how fundamental discovery can ultimately impact applied health applications. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

这项授予加州大学圣巴巴拉分校的国家科学基金会研究培训(NRT)奖将开发、实施和测试以数据驱动生物学(DDB)为主题的创新研究生教育模式。该计划的目标是培养新一代生物科学家和工程师，他们可以跨学科工作，熟练掌握数据分析和实验方法，并将推动数量生物学和生物工程的基础研究。细胞和发育生物学正在从定性观测科学过渡到利用物理学和工程学的建模和设计原理的定量、数据丰富的领域。成像和测序技术的进步，加上机器学习和计算机视觉工具，正在对定量细胞生物学产生革命性的影响。为了充分利用这些技术，现代生物工程师需要精通生物实验的设计和数据挖掘策略，以整合跨尺度(从时间和从遗传/分子到细胞和组织尺度)的信息。DDB旨在为学生提供有意义的跨学科协作的广度和深度，以数据科学方法的知识和经验为支持，以科学的严谨性回答生物学问题。该项目预计将培养70名博士生，其中包括30名受资助的实习生，他们的博士生项目包括：生物工程、生物分子科学与工程、化学工程、计算机科学、电气与计算机工程、分子、细胞与发育生物学、机械工程和物理。通过DDB，学生将学习如何设计实验；获取和整合多模式、不同的数据；以及整合机器学习和计算方法以从生物数据中提取模式和意义，以理解和利用干细胞衍生模型中的异质性。受训者将得到新课程的支持，该课程将作为新兴生物工程博士项目的基础。入门课程将包括一门关于开创性研究论文和设计跨学科调查最佳实践的结构化课程。有了这些培训元素，学生们将沉浸在体内研究体验中，进行开创性实验的动手重新设计，并亲自实施先进的研究方法来检验这些开创性论文的结论。学生们还将参与不同实验室(实验和模型)中共同指导的研究轮换项目。为了支持自我反思、深思熟虑的职业规划和自我效能，该项目将部署一个三管齐下的指导计划，包括教师顾问、同行反馈和通过个人发展计划进行自我评估。最后，实习和外部培训将通过致力于融合和翻译培训的研究网络为学员提供身临其境的交流机会。这将使学生亲身体验基础发现如何最终影响应用的健康应用。NSF研究培训(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革意义的STEM研究生教育培训模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。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。