We conclude that we have a dynamical model that teaches us how to extract the entire information content of Dictyostelium trajectories produced by tracking the centroid of the cell’s perimeter. The features of our model are generic in nature, so the model will probably also describe the trajectories of other cell types with amoeboid motion. For Dictyostelium, we note that individuality in cells is evident in and in the fitted parameter values (Tables, Supporting Material
). The challenge now is to relate these parameters to biology. This is optimally done in experiments that pursue how parameter values depend on other differences observed in cells, and how they respond to changes in stimuli, environment, and within cells. The model is optimally suited, we believe, for capturing even small changes quantitatively when they affect trajectories. This we base on its ability to capture individuality of cells via their trajectories and on a comparison with two alternatives: The run-and-turn description, which is much simpler, but does force an a priori interpretation on trajectories, and the description in terms of contours, which is more detailed with fine resolution of pseudopod activity, but either limited to short time series—time-series of contours shown as in [36
, and ] and [37
] simply must be short—or must be compressed by further processing, as done elegantly in [11
Two qualitative, conceptual results were already produced by our insistence on quantitative modeling of every detail in the data: (i) We resolved more than one time scale in Dictyostelium dynamics, the longest being the persistence time for the general direction of motion that is left, after the faster time scale of the quasi-periodic left-right waddle has been filtered away. (ii) We found that this longest time scale is so long that many trajectories actually are too short to self-average properly, which explains why some workers thought they saw super-diffusive behavior in Dictyostelium trajectories [20
]. Such behavior requires a truly exceptional memory of spatial orientation from the cell, as any reasonable mechanism of memory looses memory at a constant rate, resulting in exponentially decreasing correlations, and hence normal diffusive behavior on time-scales much longer than the correlation time. This result of our top-down phenomenological, quantitative approach—that Dictyostelium motility can be understood without mechanisms of memory that one cannot imagine a physical/chemical/biological basis for—simplifies the target of the biochemical bottom-up approach immensely.
Thus our dynamical model’s precise capture of macroscopic Dictyostelium motility provides a natural benchmark for more detailed understandings of motility. Conversely, input from more detailed understandings must be the future of macroscopic motility modeling, now that the limiting factor no longer is lack of experimental data. Only modeling with a solid biophysical/biochemical basis in the vein of [2
] can filter the information-deluge that is possible now that time-resolution and duration of measurements are limited only by what is meaningful for a given cell type, and hundreds or thousands of data can be recorded for a single cell at every time-lapse.