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Robust Actin Filament Modeling in Low-Resolution Cryo-ET Images Using Advanced Deep Learning and Algorithmic Techniques

Award Number: R16GM165753
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 07/16/2026
PERIOD OF PERFORMANCE END DATE: 04/30/2030

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Robust Actin Filament Modeling in Low-Resolution Cryo-ET Images Using Advanced Deep Learning and Algorithmic Techniques - Project Summary Dysregulation of the actin cytoskeleton drives the pathogenesis of chronic diseases, including cancer metastasis, cardiovascular dysfunction, and neurodegenerative conditions, which collectively contribute to over 30 million deaths annually. The ability to quantitatively characterize actin filament architecture within its native cellular context is essential for understanding the structural basis of this dysregulation. Cryo-electron tomography (cryo-ET) has emerged as a uniquely powerful technique for visualizing macro- molecular complexes like actin filaments within their native cellular context; however, extracting accurate, quantitative morphological data is severely hampered by technical challenges. These include intrinsically low signal-to-noise ratios exacerbated by the missing wedge artifact, and a critical lack of robust, auto- mated computational tools modeling the complex, densely packed actin networks. This project proposes to address these limitations by developing and validating a novel, integrated computational framework specifically designed for the analysis of actin filaments from cryo-ET data. Our central hypothesis is that a tailored approach combining filament-specific image enhancement, advanced deep learning, and geometry-aware reconstruction will overcome bottlenecks and enable unprecedented quantitative assessment of actin morphology in native states. To test this hypothesis, we will pursue three synergistic specific aims: (1) We will develop a local dynamic programming (DP) algorithm to enhance the contrast of filamentous structures by leveraging their linear geometry to amplify coherent signals against noise. (2) We will create a dedicated deep learning segmentation tool, pre-trained on large- scale simulated datasets with realistic noise profiles and fine-tuned on experimental data, to achieve accurate and generalizable identification of actin filaments across cellular contexts. (3) We will implement a reconstruction pipeline that incorporates filament-specific clustering and directional analysis to convert segmented volumes into discrete, biologically plausible 3D models. The successful completion of these aims will deliver a comprehensive, open-source computational framework for automated and reproducible analysis of actin network architecture, helping to elucidate the connection between structural alterations and disease-specific outcomes. The expected outcomes include mechanistic insights into actin-driven disease processes, laying the groundwork for future diag- nostic and therapeutic innovations. Furthermore, this interdisciplinary project aligns with the NIH Support for Research Excellence (SuRE) mission by providing hands-on research training for undergraduate and graduate students, thereby strengthening biomedical research capacity at Georgia Southern University.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $142,000 )
2026	2026	GEORGIA SOUTHERN UNIVERSITY RESEARCH & SERVICE FOUNDATION INC	261 FOREST DR STE 3000	STATESBORO	GA	30458	BULLOCH	USA	Biomedical Research and Research Training	000	1	7/16/2026	NEW	$142,000
														Subtotal = $142,000

Grand Total All Awards = $142,000

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Robust Actin Filament Modeling in Low-Resolution Cryo-ET Images Using Advanced Deep Learning and Algorithmic Techniques

Award Number: R16GM165753

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 07/16/2026

PERIOD OF PERFORMANCE END DATE: 04/30/2030

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