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Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection.

Mark N Read1,2, Jacqueline Bailey3, Jon Timmis4

  • 1School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia.

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|September 3, 2016
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Summary
This summary is machine-generated.

Correlated random walks best model immune cell motility in vivo. This approach, using multi-objective optimization, accurately captures complex cellular movement dynamics, improving computational simulations of immune responses.

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Area of Science:

  • Immunology
  • Computational Biology
  • Biophysics

Background:

  • Two-photon microscopy provides detailed in vivo cellular motility data.
  • Understanding immune cell movement is crucial for immune response research.
  • Computational simulations are vital for studying immune processes.

Purpose of the Study:

  • To evaluate different computational models for simulating in vivo immune cell motility.
  • To determine which motility model best reflects the complex dynamics of neutrophils and T cells.
  • To develop a robust method for selecting and parameterizing motility models.

Main Methods:

  • Compared Brownian motion, Lévy walk, and correlated random walks (CRWs).
  • Utilized multi-objective optimization to parameterize models against multiple motility metrics simultaneously.
  • Analyzed cellular translational speed, turn speed, and meandering indices.

Main Results:

  • Correlated random walks (CRWs) significantly improved the capture of in vivo motility data for both neutrophils and T cells.
  • Inverse correlation between translational and turn speeds further enhanced CRW model accuracy.
  • Brownian motion poorly reflected the observed cellular dynamics, while Lévy walk showed limited success.

Conclusions:

  • CRWs provide the most accurate representation of in vivo immune cell motility among the tested models.
  • Multi-objective optimization is an effective technique for selecting and validating computational models against complex biological data.
  • Accurate simulation of immune cell motility dynamics is essential for advancing computational immunology.