New method for modeling connective-tissue cell migration: improved accuracy on motility parameters.

Mathematical models of cell migration based on persistent random walks have been successfully applied to describe the motility of several 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 identify 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 is considerably heterogeneous with respect to cell speed. Here we develop a method for fitting connective-tissue cell migration data to persistent random walk models, 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 motility 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 cell migration.