Improved estimation of anomalous diffusion exponents in single particle tracking experiments
arXiv:1212.0793 · doi:10.1103/PhysRevE.87.052713
Abstract
The Mean Square Displacement is a central tool in the analysis of Single Particle Tracking experiments, shedding light on various biophysical phenomena. Frequently, parameters are extracted by performing time-averages on single particle trajectories followed by ensemble averaging. This procedure however, suffers from two systematic errors when applied to particles that perform anomalous diffusion. The first is significant at short time lags and is induced by measurement errors. The second arises from the natural heterogeneity in biophysical systems. We show how to estimate and correct these two errors and improve the estimation of the anomalous parameters for the whole particle distribution. As a consequence we manage to characterize ensembles of heterogeneous particles even for rather short and noisy measurements where regular time averaged mean square displacement analysis fails. We apply this method to both simulations and in vivo measurements of telomere diffusion in 3T3 mouse embryonic fibroblast cells. The motion of telomeres is found to be subdiffusive with an average exponent constant in time. Individual telomere exponents are normally distributed around the average exponent. The proposed methodology has the potential to improve experimental accuracy while maintaining lower experimental costs and complexity.
13 pages, 4 figures
References in corpus (3)
Cited by in corpus (28)
- From a melt of rings to chromosome territories: The role of topological constraints in genome folding
- Elucidating the Origin of Heterogeneous Anomalous Diffusion in the Cytoplasm of Mammalian Cells
- Machine learning method for single trajectory characterization
- Measurement of Anomalous Diffusion Using Recurrent Neural Networks
- Noisy continuous time random walks
- Anomalous diffusion in fractal globules
- Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
- Understanding the dynamics of rings in the melt in terms of annealed tree model
- Heterogeneities Shape Passive Intracellular Transport
- Learning physical properties of anomalous random walks using graph neural networks
- Ergodicity breaking and particle spreading in noisy heterogeneous diffusion processes
- Inferring pointwise diffusion properties of single trajectories with deep learning
- Moses, Noah and Joseph Effects in Coupled Lévy Processes
- Anomalous diffusions induced by enhancement of memory
- Particle transport in hybrid PIC shock simulations: A comparison of diagnostics
- Log it: How to fit an active Brownian particle's mean squared displacement with improved parameter estimation
- Superstatistical approach of the anomalous exponent for scaled Brownian motion
- Influence of external potentials on heterogeneous diffusion processes
- Apparent superballistic dynamics in one-dimensional random walks with biased detachment
- Robust Hypothesis Tests for Detecting Statistical Evidence of 2D and 3D Interactions in Single-Molecule Measurements
- The pseudo-two-dimensional dynamics in a system of macroscopic rolling spheres
- Fitting a function to time-dependent ensemble averaged data
- Fluid heterogeneity detection based on the asymptotic distribution of the time-averaged mean squared displacement in single particle tracking experiments
- Rotational and translational drags of a Janus particle close to a wall and a lipid membrane
- Non-stationary Markovian Replication Process causing Diverse Diffusions
- Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories
- Recurrent neural network analysis of single trajectories switching between anomalous diffusion states
- Improved mean squared displacement analysis for anomalous single particle trajectories