Abstract
Variability in cell properties can be an important driving mechanism behind spatiotemporal patterns in biological systems, as the degree of cell-to-cell differences determines the capacity of cells to locally synchronize and, consequently, form patterns on a larger spatial scale. In principle, certain features of spatial patterns emerging with time may be regulated by variability or, more specifically, by certain constellations of cell-to-cell differences. Similarly, measuring variability in a system (i.e. the spatial distribution of cell-cell differences) may help predict properties of later-stage patterns.Here we apply and compare different statistical methods of extracting such systematic cell-to-cell differences in the case of patterns generated with a simple model system of an excitable medium and of experimental data by the slime mold Dictyostelium discoideum. We demonstrate with the help of a correlation analysis that these methods produce systematic (i.e. stationary) results for cell properties. Furthermore, we discuss possible applications of our method, in particular how these cell properties may serve as predictors of certain later-stage patterns.