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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of California-Santa Barbara |
| Country | United States |
| Start Date | Sep 01, 2021 |
| End Date | Aug 31, 2026 |
| Duration | 1,825 days |
| Number of Grantees | 4 |
| Roles | Principal Investigator; Co-Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2125644 |
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.
University of California-Santa Barbara
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