Application of artificial intelligence strategies to the analysis of neurotransmitter receptor dynamics in living cells
Abstract: Storm (stochastical optical reconstruction microscopy), a form of single-molecule nanoscopy, calls for a variety of statistical and mathematical operations to reconstruct the original objects from their noisy wide-field point spread functions [1]. We are interested in understanding the d...
Guardado en:
| Autores principales: | , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Cambridge University Press
2022
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| Materias: | |
| Acceso en línea: | https://repositorio.uca.edu.ar/handle/123456789/14612 |
| Aporte de: |
| Sumario: | Abstract: Storm (stochastical optical reconstruction microscopy), a form of single-molecule nanoscopy,
calls for a variety of statistical and mathematical operations to reconstruct the original objects from
their noisy wide-field point spread functions [1]. We are interested in understanding the dynamics
of the nicotinic acetylcholine receptor (nAChR) protein, a cell-surface neurotransmitter receptor.
Analyzing the translational motion of nAChR molecules by single-particle tracking in living cells
is a complex task. In order to understand how nAChR molecules associate/dissociate into/from
nanometer-sized clusters over time, and to characterize their trajectories according to different
mathematical models, we are developing analytical procedures based on artificial intelligence. Due
to their speed of calculation and accuracy, deep learning models are clearly an improvement on
classical models in biological image analysis and biomedical science. |
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