New Method for Modeling Connective-Tissue Cell Migration: Improved Accuracy on Motility Parameters
Matt J. Kipper, Hynda K. Kleinman, Francis W. Wang
Mathematical models of cell migration based on persistent random walks have been successfully applied to describe the motility of many cell types. However, the migration of slowly moving connective tissue cells, such as fibroblasts, is difficult to observe experimentally and difficult to describe theoretically. We have identified two primary sources of this difficulty. First, cells such as fibroblasts tend to migrate slowly and change shape during migration. This makes accurate determination of cell position difficult. Second, the cell population contains considerable inhomogeneity with respect to cell speed. Here we develop a method for modeling connective tissue cell migration, which accounts for these two significant sources of error and enables accurate determination of the cell motility parameters.We demonstrate the usefulness of this method for modeling both isotropic cell motility and biased cell motility, where the migration of a population of cells is influenced by a gradient in a surface bound adhesive peptide. This method can discern differences in the mobility of populations of cells at different points along the peptide gradient and can therefore be used as a tool to quantify the effects of peptide concentration and gradient magnitude on the cell migration.
fibroblast, haptokinesis, haptotaxis, peptide gradient, persistent random walk
, Kleinman, H.
and Wang, F.
New Method for Modeling Connective-Tissue Cell Migration: Improved Accuracy on Motility Parameters, Biophysical Journal
(Accessed May 31, 2023)