A single-particle tracking framework for quantifying intracellular partition free energies
Abstract
In recent years, the segmentation of short molecular trajectories with varying diffusive properties has drawn particular attention of researchers, since it allows studying the dynamics of a particle. In the past decade, machine learning methods have shown highly promising results, also in changepoint detection and segmentation tasks. Here, we introduce a novel iterative method to identify the changepoints in a molecular trajectory, i.e. frames, where the diffusive behavior of a particle changes. A trajectory in our case follows a fractional Brownian motion and we estimate the diffusive properties of the trajectories. The proposed Bottom-up iterative anomalous diffusion detector (BI-ADD) combines unsupervised and supervised learning methods to detect the changepoints. Our approach can be used for the analysis of molecular trajectories at the individual level and also be extended to multiple particle tracking, which is an important challenge in fundamental biology. We validated BI-ADD in various scenarios within the framework of the 2nd anomalous diffusion challenge 2024 dedicated to single particle tracking. Our method is implemented in Python and is publicly available for research purposes.
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BibTeX
@article{nordentoft2026singleparticle,
title = {{A single-particle tracking framework for quantifying intracellular partition free energies}},
author = {Malthe Nordentoft and Emilie Jessen and Enora Corsin and Judith Miné-Hattab and Mogens Jensen and Mathias Heltberg},
journal = {Biophysical Journal},
year = {2026},
month = oct,
doi = {10.1016/j.bpj.2026.09.023},
url = {https://hal.science/hal-05787196v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - A single-particle tracking framework for quantifying intracellular partition free energies
AU - Malthe Nordentoft
AU - Emilie Jessen
AU - Enora Corsin
AU - Judith Miné-Hattab
AU - Mogens Jensen
AU - Mathias Heltberg
PY - 2026
DA - 2026/10/08
JO - Biophysical Journal
DO - 10.1016/j.bpj.2026.09.023
UR - https://hal.science/hal-05787196v1
AB - In recent years, the segmentation of short molecular trajectories with varying diffusive properties has drawn particular attention of researchers, since it allows studying the dynamics of a particle. In the past decade, machine learning methods have shown highly promising results, also in changepoint detection and segmentation tasks. Here, we introduce a novel iterative method to identify the changepoints in a molecular trajectory, i.e. frames, where the diffusive behavior of a particle changes. A trajectory in our case follows a fractional Brownian motion and we estimate the diffusive properties of the trajectories. The proposed Bottom-up iterative anomalous diffusion detector (BI-ADD) combines unsupervised and supervised learning methods to detect the changepoints. Our approach can be used for the analysis of molecular trajectories at the individual level and also be extended to multiple particle tracking, which is an important challenge in fundamental biology. We validated BI-ADD in various scenarios within the framework of the 2nd anomalous diffusion challenge 2024 dedicated to single particle tracking. Our method is implemented in Python and is publicly available for research purposes.
SN - 0006-3495
SN - 1542-0086
ER -