Maximum likelihood-based estimation of diffusion coefficient is quick and reliable method for analyzing estradiol actions on surface receptor movements.
MLE
diffusion coefficient
maximum likelihood
mean square displacement
receptor movements
Journal
Frontiers in neuroinformatics
ISSN: 1662-5196
Titre abrégé: Front Neuroinform
Pays: Switzerland
ID NLM: 101477957
Informations de publication
Date de publication:
2023
2023
Historique:
received:
02
08
2022
accepted:
21
02
2023
entrez:
27
3
2023
pubmed:
28
3
2023
medline:
28
3
2023
Statut:
epublish
Résumé
The rapid effects of estradiol on membrane receptors are in the focus of the estradiol research field, however, the molecular mechanisms of these non-classical estradiol actions are poorly understood. Since the lateral diffusion of membrane receptors is an important indicator of their function, a deeper understanding of the underlying mechanisms of non-classical estradiol actions can be achieved by investigating receptor dynamics. Diffusion coefficient is a crucial and widely used parameter to characterize the movement of receptors in the cell membrane. The aim of this study was to investigate the differences between maximum likelihood-based estimation (MLE) and mean square displacement (MSD) based calculation of diffusion coefficients. In this work we applied both MSD and MLE to calculate diffusion coefficients. Single particle trajectories were extracted from simulation as well as from α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor tracking in live estradiol-treated differentiated PC12 (dPC12) cells. The comparison of the obtained diffusion coefficients revealed the superiority of MLE over the generally used MSD analysis. Our results suggest the use of the MLE of diffusion coefficients because as it has a better performance, especially for large localization errors or slow receptor movements.
Identifiants
pubmed: 36970656
doi: 10.3389/fninf.2023.1005936
pmc: PMC10031098
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1005936Informations de copyright
Copyright © 2023 Makkai, Abraham, Barabas, Godo, Ernszt, Kovacs, Kovacs, Szocs and Janosi.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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