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Chemotaxis, the directed motion of a cell towards a chemical source, plays a key
role in a wide variety of biological processes. In this study, we derive a statistical description of eukaryotic chemotaxis. We describe directional cell migration as the interplay between deterministic and stochastic contributions and investigate how they vary with the gradient strength. The functional forms of the corresponding components in the underlying Langevin equation are directly extracted from experimental data by angle-resolved conditional averages.
All experiments are performed with the social amoeba Dictyostelium discoideum, a model organism for eukaryotic chemotaxis. In agreement with earlier work on random (non-chemotactic) motion of cells, we find linear deterministic damping and multiplicative noise. In the presence of an external gradient, the deterministic part shows a clear angular dependence that takes the form of a force pointing in gradient direction. With increasing gradient steepness, this force passes through a maximum that coincides with maxima in both speed and directionality of the cells. The stochastic part, on the other hand, does not depend on the orientation of the directional cue and remains independent of the gradient magnitude. Numerical simulations of our probabilistic model yield quantitative agreement with the experimental distribution functions. |
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