Anomalous transport in the crowded world of biological cells

By Felix Höfling, T. Franosch. Reports on progress in physics. Physical Society, 2013

1,178 citations recorded by the source corpus.

A ubiquitous observation in cell biology is that the diffusive motion of macromolecules and organelles is anomalous, and a description simply based on the conventional diffusion equation with diffusion constants measured in dilute solution fails. This is commonly attributed to macromolecular crowding in the interior of cells and in cellular membranes, summarizing their densely packed and heterogeneous structures. The most familiar phenomenon is a sublinear, power-law increase of the mean-square displacement (MSD) as a function of the lag time, but there are other manifestations like strongly reduced and time-dependent diffusion coefficients, persistent correlations in time, non-Gaussian distributions of spatial displacements, heterogeneous diffusion and a fraction of immobile particles. After a general introduction to the statistical description of slow, anomalous transport, we summarize some widely used theoretical models: Gaussian models like fractional Brownian motion and Langevin equations for visco-elastic media, the continuous-time random walk model, and the Lorentz model describing obstructed transport in a heterogeneous environment. Particular emphasis is put on the spatio-temporal properties of the transport in terms of two-point correlation functions, dynamic scaling behaviour, and how the models are distinguished by their propagators even if the MSDs are identical. Then, we review the theory underlying commonly applied experimental techniques in the presence of anomalous transport like single-particle tracking, fluorescence correlation spectroscopy (FCS) and fluorescence recovery after photobleaching (FRAP). We report on the large body of recent experimental evidence for anomalous transport in crowded biological media: in cyto- and nucleoplasm as well as in cellular membranes, complemented by in vitro experiments where a variety of model systems mimic physiological crowding conditions. Finally, computer simulations are discussed which play an important role in testing the theoretical models and corroborating the experimental findings. The review is completed by a synthesis of the theoretical and experimental progress identifying open questions for future investigation.

Subfields: Modeling and Simulation, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/52f9abbb97ef9754394e6f79ff0ed3d0a20480f1

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Citations in the corpus

Protein Crowding in Lipid Bilayers Gives Rise to Non-Gaussian Anomalous Lateral Diffusion of Phospholipids and Proteins (2016, background): “diffusion models, such as the anomalous diffusion in fractal space [32,115], the free-volume jump diffusion [116,117], or the nonergodic continuous time random walk model”
"Green" electronics: biodegradable and biocompatible materials and devices for sustainable future. (2014, positive): “41,76 To make matters more complicated for the electronic device designer, this slow and anomalous transport has to be coupled with fast and predictable electronic transport in the electronic circuit.”
Aging renewal theory and application to random walks (2013, neutral): “[43,44,51,88,89].”
Dynamics of proteins in solution (2019, methodology): “…on diffusion as observed by FCS have reported a subdiffusive, anomalous behavior, other observations were consistent with normal Brownian diffusion (Höfling and Franosch, 2013), and even unobstructed Brownian diffusion was reported for GFP in the cell cytoplasm on the 1 µs timescale, and explained…”
Random diffusivity from stochastic equations: comparison of two models for Brownian yet non-Gaussian diffusion (2018, background): “With the advance of modern microscopy techniques, in particular, superresolution microscopy, as well as massive progress in supercomputing, anomalous diffusion of the type (3) has been reported in numerous complex and biological systems [13, 14].”
Numerical solution for diffusion equations with distributed order in time using a Chebyshev collocation method (2017, background): “Although this linear growth of the mean squared displacement is observed in most diffusion problems, there is a considerable number of physical processes that do not show this behaviour ([4, 51, 50, 32, 52]), but present an anomalous diffusion, that can be subdiffusive or supperdifusive.”
Numerical solution of time fractional diffusion systems (2017, background): “Conventional MRI studies are based on the assumption of Gaussian diffusion, but biological tissues are structurally heterogeneous and under large diffusion weighting gradients the acquired signal has a heavy tail which is characteristic of anomalous diffusion [18] and hence fractional diffusion mode”