ArticleeLife2020
Deciphering anomalous heterogeneous intracellular transport with neural networks.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Characterizing cellular receptor dynamics in live fluorescence imaging using stochastic modeling and machine learning.iScience · 2026Article
- Mapping Protein Diffusion from the Plasma Membrane to the Nucleus: Insights from Fluorescence Correlation Spectroscopy.Biomolecules · 2026Review
- Unraveling the Dynamics of Oxytocin in Hypothalamic Neurons.Traffic (Copenhagen, Denmark) · 2026Article
- Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data.Cells · 2026Review
- Cytoplasmic crowding acts as a porous medium reducing macromolecule diffusion.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Quantitative evaluation of methods to analyze motion changes in single-particle experiments.Nature communications · 2025Article
- Enhancing fluorescence correlation spectroscopy with machine learning to infer anomalous molecular motion.Biophysical journal · 2025Article
- Heterogeneous model for superdiffusive movement of dense core vesicles in C. elegans.Scientific reports · 2025Article
- The viscoelasticity of high concentration monoclonal antibodies using particle tracking microrheology.APL bioengineering · 2024Article
- Ensemble heterogeneity mimics ageing for endosomal dynamics within eukaryotic cells.Scientific reports · 2023Article
- Phase-sensitive detection of anomalous diffusion dynamics in the neuronal membrane induced by ion channel gating.Physics in medicine and biology · 2023Article
- Population heterogeneity in the fractional master equation, ensemble self-reinforcement, and strong memory effects.Physical review. E · 2023Article
- The Fokker-Planck equation of the superstatistical fractional Brownian motion with application to passive tracers inside cytoplasm.Royal Society open science · 2022Article
- Article
- Local Analysis of Heterogeneous Intracellular Transport: Slow and Fast Moving Endosomes.Entropy (Basel, Switzerland) · 2021Article
- Modelling the motion of organelles in an elongated cell via the coordination of heterogeneous drift-diffusion and long-range transport.The European physical journal. E, Soft matter · 2021Article
- An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behavior. Characterization of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity. Here, we developed a deep learning feedforward neural network trained on fractional Brownian motion, providing a novel, accurate and efficient method for resolving heterogeneous behavior of intracellular transport in space and time. The neural network requires significantly fewer data points compared to established methods. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, fractional Brownian motion with a stochastic Hurst exponent was used to interpret, for the first time, anomalous intracellular dynamics, revealing unexpected differences in behavior between closely related endocytic organelles.
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Registered trials
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