Memory effects and Lévy walk dynamics in intracellular transport of cargoes

Fedotov, Sergei; Korabel, Nickolay; Waigh, Thomas A.; Han, Daniel; Allan, Victoria

doi:10.1103/physreve.98.042136

Cited by 37 publications

(52 citation statements)

References 46 publications

(60 reference statements)

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“…In recent experimental studies and modeling [66,67], the authors observed that active cargo transport tends to self-organize into Lévy walks. More specifically it has been established that the biomolecule moves along a microtubule driven by motors with a global constant velocity for some random time and then detach from the microtubule and reattaches to a new microtubule moving along another direction [62].…”

Section: Anomalous Superdiffusionmentioning

confidence: 99%

“…Active intracellular transport can overcome this difficulty so that motion is faster and direct specific. The particles (called in this context cargo) are carried by molecular motors along the is Brownian; the purple trajectory is from a Brownian motion with drift (67) and illustrates superdiffusion; the red trajectory is from an fBm (22) (parameter h > 1/2) and illustrates superdiffusion; the cyan trajectory is from an Ornstein-Uhlenbeck process (62) and illustrates confined diffusion; the green trajectory is from an fBm (22) (h < 1/2) and illustrates anomalous diffusion.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An overview of diffusion models for intracellular dynamics analysis

Briane

Vimond

Kervrann

2019

Briefings in Bioinformatics

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We present an overview of diffusion models commonly used for quantifying the dynamics of intracellular particles (e.g. biomolecules) inside eukaryotic living cells. It is established that inference on the modes of mobility of molecules is central in cell biology since it reflects interactions between structures and determines functions of biomolecules in the cell. In that context, Brownian motion is a key component in short distance transportation (e.g. connectivity for signal transduction). Another dynamical process that has been heavily studied in the past decade is the motor-mediated transport (e.g. dynein, kinesin and myosin) of molecules. Primarily supported by actin filament and microtubule network, it ensures spatial organization and temporal synchronization in the intracellular mechanisms and structures. Nevertheless, the complexity of internal structures and molecular processes in the living cell influence the molecular dynamics and prevent the systematic application of pure Brownian or directed motion modeling. On the one hand, cytoskeleton density will hinder the free displacement of the particle, a phenomenon called subdiffusion. On the other hand, the cytoskeleton elasticity combined with thermal bending can contribute a phenomenon called superdiffusion. This paper discusses the basics of diffusion modes observed in eukariotic cells, by introducing the essential properties of these processes. Applications of diffusion models include protein trafficking and transport and membrane diffusion.

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Section: Anomalous Superdiffusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An overview of diffusion models for intracellular dynamics analysis

Briane

Vimond

Kervrann

2019

Briefings in Bioinformatics

View full text Add to dashboard Cite

show abstract

“…fundamentally important because much research has focused on inferring active and passive states of transport within living cells using position-derived quantities such as windowed MSDs, directionality and velocity 31,25 . The trajectories are then segmented and Hurst exponents measured in an effort to characterise the behaviour of different cargo when they are actively transported by motor proteins 23,7 or sub-diffusing in the cytoplasm 32 . However, conventional methods such as the MSD and TAMSD need trajectories with many time points ( ∼ 10 2 − 10 3 ) to calculate a single Hurst exponent value with high fidelity.…”

Section: Dlfnn Allows Analysis Of Trajectories With Local Stochastic mentioning

confidence: 99%

“…It is vitally important to be able to quantitatively characterise the dynamics of organelles and cellular responses to different biological conditions 4,5,6 . Classification of different non-Brownian dynamic behaviours at various time scales has been crucial to the analysis of intracellular dynamics 7,8 , protein crowding in the cell 8,9, microrheology 10,11 , entangled actin networks 12 , and the movement of lysosomes 13 and endosomes 14 . Anomalous transport is currently analysed by statistical averaging methods and this has been a barrier to understanding the nature of heterogeneous anomalous transport.…”

Section: Introductionmentioning

confidence: 99%

Deciphering anomalous heterogeneous intracellular transport with neural networks

Han

Korabel

Chen

et al. 2019

Preprint

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Biological intracellular transport is predominantly heterogeneous in both time and 12 space, exhibiting varying non-Brownian behaviour. Characterisation of this movement through 13 averaging methods over an ensemble of trajectories or over the course of a single trajectory often 14 fails to capture this heterogeneity adequately. Here, we have developed a deep learning 15 feedforward neural network trained on fractional Brownian motion, which provides a novel, 16 accurate and efficient characterization method for resolving heterogeneous behaviour of 17 intracellular transport both in space and time. Importantly, the neural network requires 18 significantly fewer data points compared to established methods, such as mean square 19 displacements, rescaled range analysis and sequential range analysis. This enables robust 20 estimation of Hurst exponents for very short time series data, making possible direct, dynamic 21 segmentation and analysis of experimental tracks of rapidly moving cellular structures such as 22 endosomes and lysosomes. By using this analysis, we were able to interpret anomalous 23 intracellular dynamics as fractional Brownian motion with a stochastic Hurst exponent. 24 25

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“…Their trajectories could be best described by a non-Gaussian superdiffusive model, Lévy walk. Compared to random Brownian motion, Lévy walks could lead to highly efficient long-range transport in complex fluid [37][38][39] . Therefore, mediated by the swarm fluid, the highly active bacteria community create a dynamically well-organized flow transport network above their own bodies, possibly providing a long-range communication avenue for the population.…”

Section: Introductionmentioning

confidence: 99%

Single Nanoparticle Tracking Reveals Efficient Long-Distance Undercurrent Transport above Swarming Bacteria

Feng

Zhang²,

Wen

et al. 2019

Preprint

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Flagellated bacteria move collectively in a swirling pattern on agar surfaces immersed in a thin layer of viscous "swarm fluid", but the role of this fluid in mediating the cooperation of the bacterial population is not well understood. Herein, we use gold nanorods (AuNRs) as single particle tracers to explore the spatiotemporal structure of the swarm fluid. We observed that individual AuNRs are transported in a plane of ~2 µm above the motile cells. They can travel for long distances (~800 μm) in a 2D plane at high speed without interferences from bacterial movements. The particles are apparently lifted up and transported by collective mixing of the small vortices around bacteria during localized clustering and de-clustering of the motile cells, exhibiting superdiffusive and non-Gaussian characteristics with alternating large-step jumps and confined lingering. Their motions are consistent with the Lévy walk (LW) model, revealing efficient transport flows above swarms. These flows provide obstacle-free highways for long-range material transportations, shed light on how swarming bacteria perform population-level communications, and reveal the essential role of the fluid phase on the emergence of large-scale synergy. This approach is promising for probing complex fluid dynamics and transports in other collective systems.

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Memory effects and Lévy walk dynamics in intracellular transport of cargoes

Cited by 37 publications

References 46 publications

An overview of diffusion models for intracellular dynamics analysis

An overview of diffusion models for intracellular dynamics analysis

Deciphering anomalous heterogeneous intracellular transport with neural networks

Single Nanoparticle Tracking Reveals Efficient Long-Distance Undercurrent Transport above Swarming Bacteria

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