PUBLICATION

Inference of random walk models to describe leukocyte migration

Authors
Jones, P.J., Sim, A., Taylor, H.B., Bugeon, L., Dallman, M.J., Pereira, B., Stumpf, M.P., Liepe, J.
ID
ZDB-PUB-150926-9
Date
2015
Source
Physical Biology   12: 066001 (Journal)
Registered Authors
Bugeon, Laurence, Dallman, Maggie
Keywords
none
MeSH Terms
  • Animals
  • Bayes Theorem
  • Cell Movement*
  • Leukocytes/physiology
  • Macrophages/physiology*
  • Models, Biological
  • Neutrophils/physiology*
  • Zebrafish/physiology*
PubMed
26403334 Full text @ Phys. Biol.
Abstract
While the majority of cells in an organism are static and remain relatively immobile in their tissue, migrating cells occur commonly during developmental processes and are crucial for a functioning immune response. The mode of migration has been described in terms of various types of random walks. To understand the details of the migratory behaviour we rely on mathematical models and their calibration to experimental data. Here we propose an approximate Bayesian inference scheme to calibrate a class of random walk models characterized by a specific, parametric particle re-orientation mechanism to observed trajectory data. We elaborate the concept of transition matrices (TMs) to detect random walk patterns and determine a statistic to quantify these TM to make them applicable for inference schemes. We apply the developed pipeline to in vivo trajectory data of macrophages and neutrophils, extracted from zebrafish that had undergone tail transection. We find that macrophage and neutrophils exhibit very distinct biased persistent random walk patterns, where the strengths of the persistence and bias are spatio-temporally regulated. Furthermore, the movement of macrophages is far less persistent than that of neutrophils in response to wounding.
Genes / Markers
Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping