Simulating and predicting entangled DNA contours via deep learning

Maged F. Serag; Satoshi Habuchi

doi:10.1117/12.3027411

4 October 2024 Simulating and predicting entangled DNA contours via deep learning

Maged F. Serag, Satoshi Habuchi

Author Affiliations +

Proceedings Volume 13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; 131180B (2024) https://doi.org/10.1117/12.3027411
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Conference Poster

Abstract

We developed a computational model to simulate contours of entangled lambda DNA. These simulations were used to generate super-resolution DNA images for training a deep neural network (ANNA-PALM) to reconstruct DNA contours from localization images. Our approach enabled reliable contour prediction from microscopy images captured at fast time scale. Analysis of experimental data revealed bright and dark DNA segments, potentially linked to local microviscosity effects imposed by entanglement loci. Our integrated computational modeling and deep learning workflow can provide mapping of topological constraints on polymer motion in diverse materials.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Maged F. Serag and Satoshi Habuchi "Simulating and predicting entangled DNA contours via deep learning", Proc. SPIE 13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, 131180B (4 October 2024); https://doi.org/10.1117/12.3027411

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