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.
| Original language | English (US) |
|---|---|
| Title of host publication | Emerging Topics in Artificial Intelligence, ETAI 2024 |
| Editors | Giovanni Volpe, Joana B. Pereira, Daniel Brunner, Aydogan Ozcan |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510678965 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 Emerging Topics in Artificial Intelligence, ETAI 2024 - San Diego, United States Duration: Aug 18 2024 → Aug 23 2024 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 13118 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Conference
| Conference | 2024 Emerging Topics in Artificial Intelligence, ETAI 2024 |
|---|---|
| Country/Territory | United States |
| City | San Diego |
| Period | 08/18/24 → 08/23/24 |
Bibliographical note
Publisher Copyright:© 2024 SPIE.
Keywords
- Deep learning
- DNA
- Dynamics
- Entanglement
- Single-Molecule Localization microscopy
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering