scholarly journals Estimating the anomalous diffusion exponent for single particle tracking data with measurement errors - An alternative approach

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Krzysztof Burnecki ◽  
Eldad Kepten ◽  
Yuval Garini ◽  
Grzegorz Sikora ◽  
Aleksander Weron
Keyword(s):  
Particle Tracking ◽  
Anomalous Diffusion ◽  
Single Particle ◽  
Measurement Errors ◽  
Single Particle Tracking ◽  
Tracking Data ◽  
Alternative Approach

scholarly journals Probing the type of anomalous diffusion with single-particle tracking

2014 ◽  
Vol 16 (17) ◽  
pp. 7686-7691 ◽  
Author(s):  
Dominique Ernst ◽  
Jürgen Köhler ◽  
Matthias Weiss
Keyword(s):  
Random Walk ◽  
Particle Tracking ◽  
Anomalous Diffusion ◽  
Single Particle ◽  
Single Particle Tracking ◽  
Tracking Data

We introduce a versatile method to extract the type of (transient) anomalous random walk from experimental single-particle tracking data.

scholarly journals Anomalous diffusion models and their properties: non-stationarity, non-ergodicity, and ageing at the centenary of single particle tracking

2014 ◽  
Vol 16 (44) ◽  
pp. 24128-24164 ◽  
Author(s):  
Ralf Metzler ◽  
Jae-Hyung Jeon ◽  
Andrey G. Cherstvy ◽  
Eli Barkai
Keyword(s):  
Particle Tracking ◽  
Anomalous Diffusion ◽  
Single Particle ◽  
Diffusion Processes ◽  
Diffusion Models ◽  
Single Particle Tracking ◽  
Diffusion Data

This Perspective summarises the properties of a variety of anomalous diffusion processes and provides the necessary tools to analyse and interpret recorded anomalous diffusion data.

scholarly journals High-Speed Single-Particle Tracking of GM1 in Model Membranes Reveals Anomalous Diffusion due to Interleaflet Coupling and Molecular Pinning

Nano Letters ◽  
2014 ◽  
Vol 14 (9) ◽  
pp. 5390-5397 ◽  
Author(s):  
Katelyn M. Spillane ◽  
Jaime Ortega-Arroyo ◽  
Gabrielle de Wit ◽  
Christian Eggeling ◽  
Helge Ewers ◽  
...  
Keyword(s):  
Particle Tracking ◽  
Anomalous Diffusion ◽  
Single Particle ◽  
High Speed ◽  
Single Particle Tracking ◽  
Model Membranes

scholarly journals Trajectories and single-particle tracking data of intracellular vesicles loaded with either SNAP-Crb3A or SNAP-Crb3B

Data in Brief ◽  
2016 ◽  
Vol 7 ◽  
pp. 1665-1669 ◽  
Author(s):  
Jan Peter Siebrasse ◽  
Ivona Djuric ◽  
Ulf Schulze ◽  
Marc A. Schlüter ◽  
Hermann Pavenstädt ◽  
...  
Keyword(s):  
Particle Tracking ◽  
Single Particle ◽  
Single Particle Tracking ◽  
Tracking Data ◽  
Intracellular Vesicles

scholarly journals Biophysical Models of PAR Cluster Transport by Cortical Flow in C. elegans Early Embryogenesis

2021 ◽  
Author(s):  
Cole Zmurchok ◽  
William R. Holmes
Keyword(s):  
Particle Tracking ◽  
Single Particle ◽  
Peclet Number ◽  
Displacement Vector ◽  
Single Particle Tracking ◽  
Tracking Data ◽  
Cortical Actin ◽  
Biophysical Parameters ◽  
Péclet Number ◽  
Protein Clusters

The clustering of membrane-bound proteins facilitates their transport by cortical actin flow in early Caenorhabditis elegans embryo cell polarity. PAR-3 clustering is critical for this process, yet the biophysical processes that couple protein clusters to cortical flow remain unknown. We develop a discrete, stochastic agent-based model of protein clustering and test four hypothetical models for how clusters may interact with the flow. Results show that the canonical way to assess transport characteristics from single particle tracking data used thus far in this area, the Péclet number, is insufficient to distinguish these hypotheses and that all models can account for transport characteristics quantified by this measure. However, using this model, we demonstrate that these different cluster-cortex interactions may be distinguished using a different metric, namely, the scalar projection of cluster displacement on to the flow displacement vector. Our results thus provide a testable way to use existing single particle tracking data to test how endogenous protein clusters may interact with the cortical flow to localize during polarity establishment. To facilitate this investigation, we also develop both improved simulation and semi-analytic methodologies to quantify motion summary statistics (e.g., Péclet number and scalar projection) for these stochastic models as a function of biophysical parameters.

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