@inproceedings{458621cd6e11445080c805f360537ead,
title = "Simulating and predicting entangled DNA contours via deep learning",
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.",
keywords = "Deep learning, DNA, Dynamics, Entanglement, Single-Molecule Localization microscopy",
author = "Serag, {Maged F.} and Satoshi Habuchi",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2024 Emerging Topics in Artificial Intelligence, ETAI 2024 ; Conference date: 18-08-2024 Through 23-08-2024",
year = "2024",
doi = "10.1117/12.3027411",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Giovanni Volpe and Pereira, {Joana B.} and Daniel Brunner and Aydogan Ozcan",
booktitle = "Emerging Topics in Artificial Intelligence, ETAI 2024",
address = "United States",
}