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Collaborative Research: Database and toolbox for characterization of subcellular components in cellular cryo-electron tomograms

NSF

open

About This Grant

Awards are made to Carnegie Mellon University and Purdue University to enable the development of a cyberinfrastructure that supports the analysis of cryo-electron tomography (cryo-ET) data. Cryo-ET is a cutting-edge imaging technology for revealing the structures and spatial organizations of subcellular components, in particular, macromolecular complexes, inside cells. This project will build an open-access, annotated database of cryo-ET images—both simulated and experimentally obtained—alongside a robust toolbox of computational methods for their analysis. The resulting resources will lower the entry barrier for new researchers, promote collaboration, and accelerate scientific discoveries across the life sciences. Educational outreach includes training Ph.D., graduate, and undergraduate students through interdisciplinary coursework, hands-on research, and workshops at both institutions. Workshops will also be held for the broader research community, including educators and students at the high school level. Beyond biology, the tools developed will support innovations in medical imaging, and materials science, ultimately contributing to workforce development in data-driven scientific fields. The intellectual merit of this project lies in establishing a foundational infrastructure for cryo-ET data analysis that addresses a critical gap in the field: the lack of well-curated, annotated datasets and standardized computational tools. By developing realistic simulated datasets, manually and semi-automatically annotated experimental data, and a benchmark database, the project supports rigorous method development and validation. The project also integrates state-of-the-art machine learning and computer vision algorithms, including novel simulation methods and segmentation frameworks. Together, these innovations will catalyze the development of new computational techniques and deepen our understanding of the structures and spatial organizations of subcellular components within cells, advancing the frontiers of structural and cell biology. 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.

Focus Areas

machine learningbiologyeducation

Eligibility

universitynonprofitsmall business

How to Apply

Funding Range

Up to $225K

Deadline

2028-07-31

Complexity
Medium
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