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Interplay Between the Persistent Random Walk and the Contact Inhibition of Locomotion Leads to Collective Cell Behaviors

  • Abdel-Rahman Hassan
  • Thomas Biel
  • David M. Umulis
  • Taeyoon KimEmail author
Special Issue: Multi-scale Modeling of Tissue Growth and Shape

Abstract

Cell migration plays an important role in physiology and pathophysiology. It was observed in the experiments that cells, such as fibroblast, leukocytes, and cancer cells, exhibit a wide variety of migratory behaviors, such as persistent random walk, contact inhibition of locomotion, and ordered behaviors. To identify biophysical mechanisms for these cellular behaviors, we developed a rigorous computational model of cell migration on a two-dimensional non-deformable substrate. Cells in the model undergo motion driven by mechanical interactions between cellular protrusions and the substrate via the balance of tensile forces. Properties of dynamic formation of lamellipodia induced the persistent random walk behavior of a migrating cell. When multiple cells are included in the simulation, the model recapitulated the contact inhibition of locomotion between cells at low density without any phenomenological assumptions or momentum transfer. Instead, the model showed that contact inhibition of locomotion can emerge via indirect interactions between the cells through their interactions with the underlying substrate. At high density, contact inhibition of locomotion between numerous cells gave rise to confined motions or ordered behaviors, depending on cell density and how likely lamellipodia turn over due to contact with other cells. Results in our study suggest that various collective migratory behaviors may emerge without more restrictive assumptions or direct cell-to-cell biomechanical interactions.

Keywords

Cell migration Simulation Persistent random walk Contact inhibition of locomotion Nematic order 

Notes

Acknowledgements

The authors gratefully acknowledge the support from the National Institute of Health (1R01GM126256). This work used the Extreme Science and Engineering Discovery Environment (XSEDE) (Moore et al. 2014; Towns et al. 2014), which is supported by National Science Foundation grant number ACI-1548562. The computations were conducted on the Comet supercomputer, which is supported by NSF Award Number ACI-1341698, at the San Diego Supercomputing Center (SDSC).

Supplementary material

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Copyright information

© Society for Mathematical Biology 2019

Authors and Affiliations

  1. 1.Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteUSA
  2. 2.Department of Agricultural and Biological EngineeringPurdue UniversityWest LafayetteUSA

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