ArticlePLoS computational biology2019
Identifying the mechanism for superdiffusivity in mouse fibroblast motility.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Anisotropic persistent random walk model simulates T-cells migration over curved landscapes.Scientific reports · 2025Article
- Microinterfaces in biopolymer-based bicontinuous hydrogels guide rapid 3D cell migration.Nature communications · 2024Article
- Tuning the Topography of Dynamic 3D Scaffolds through Functional Protein Wrinkled Coatings.Polymers · 2024Article
- Printing Parameters of Fused Filament Fabrication Affect Key Properties of Four-Dimensional Printed Shape-Memory Polymers.3D printing and additive manufacturing · 2023Article
- Random walk and cell morphology dynamics inFrontiers in cell and developmental biology · 2023Article
- Collective chemotaxis in a Voronoi model for confluent clusters.Biophysical journal · 2022Article
- Article
- Image-based cell subpopulation identification through automated cell tracking, principal component analysis, and partitioning around medoids clustering.Medical & biological engineering & computing · 2021Article
- Stochastic and Heterogeneous Cancer Cell Migration: Experiment and Theory.Scientific reports · 2019Article
- Crosshatch nanofiber networks of tunable interfiber spacing induce plasticity in cell migration and cytoskeletal response.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We seek to characterize the motility of mouse fibroblasts on 2D substrates. Utilizing automated tracking techniques, we find that cell trajectories are super-diffusive, where displacements scale faster than t1/2 in all directions. Two mechanisms have been proposed to explain such statistics in other cell types: run and tumble behavior with Lévy-distributed run times, and ensembles of cells with heterogeneous speed and rotational noise. We develop an automated toolkit that directly compares cell trajectories to the predictions of each model and demonstrate that ensemble-averaged quantities such as the mean-squared displacements and velocity autocorrelation functions are equally well-fit by either model. However, neither model correctly captures the short-timescale behavior quantified by the displacement probability distribution or the turning angle distribution. We develop a hybrid model that includes both run and tumble behavior and heterogeneous noise during the runs, which correctly matches the short-timescale behaviors and indicates that the run times are not Lévy distributed. The analysis tools developed here should be broadly useful for distinguishing between mechanisms for superdiffusivity in other cells types and environments.
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Registered trials
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