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We studied transitions between spatiotemporal patterns that can be induced in a spatially extended nonlinear chemical system by a unidirectional flow in combination with constant inflow concentrations. Three different scenarios were investigated. (i) Under conditions where the system exhibited two stable fixed points, the propagation direction of trigger fronts could be reversed, so that domains of the less stable fixed point invaded the system. (ii) For bistability between a stable fixed point and a limit cycle we observed that above a critical flow velocity, the unstable focus at the center of the limit cycle could be stabilized. Increasing the flow speed further, a regime of damped flow-distributed oscillations was found and, depending on the boundary values at the inflow, finally the stable fixed point dominated. Similarly, also in the case of spatiotemporal chaos (iii), the unstable steady state could be stabilized and was replaced by the stable fixed point with increasing flow velocity. We finally outline a linear stability analysis that can explain part of our findings.
Spatiotemporal chaos arising from standing waves in a reaction-diffusion system with cross-diffusion
(2012)
We show that quasi-standing wave patterns appear in the two-variable Oregonator model of the Belousov-Zhabotinsky reaction when a cross-diffusion term is added, no wave instability is required in this case. These standing waves have a frequency that is half the frequency of bulk oscillations displayed in the absence of diffusive coupling. The standing wave patterns show a dependence on the systems size. Regular standing waves can be observed for small systems, when the system size is an integer multiple of half the wavelength. For intermediate sizes, irregular patterns are observed. For large sizes, the system shows an irregular state of spatiotemporal chaos, where standing waves drift, merge, and split, and also phase slips may occur.
Impact of the carbazole derivative wiskostatin on mechanical stability
and dynamics of motile cells
(2012)
Many essential functions in eukaryotic cells like phagocytosis, division, and motility rely on the dynamical properties of the actin cytoskeleton. A central player in the actin system is the Arp2/3 complex. Its activity is controlled by members of the WASP (Wiskott-Aldrich syndrome protein) family. In this work, we investigated the effect of the carbazole derivative wiskostatin, a recently identified N-WASP inhibitor, on actin-driven processes in motile cells of the social ameba . Drug-treated cells exhibited an altered morphology and strongly reduced pseudopod formation. However, TIRF microscopy images revealed that the overall cortical network structure remained intact. We probed the mechanical stability of wiskostatin-treated cells using a microfluidic device. While the total amount of F-actin in the cells remained constant, their stiffness was strongly reduced. Furthermore, wiskostatin treatment enhanced the resistance to fluid shear stress, while spontaneous motility as well as chemotactic motion in gradients of cAMP were reduced. Our results suggest that wiskostatin affects the mechanical integrity of the actin cortex so that its rigidity is reduced and actin-driven force generation is impaired.
Chemotaxis, the directed motion of a cell toward a chemical source, plays a key role in many essential biological processes. Here, we derive a statistical model that quantitatively describes the chemotactic motion of eukaryotic cells in a chemical gradient. Our model is based on observations of the chemotactic motion of the social ameba Dictyostelium discoideum, a model organism for eukaryotic chemotaxis. A large number of cell trajectories in stationary, linear chemoattractant gradients is measured, using microfluidic tools in combination with automated cell tracking. We describe the directional motion as the interplay between deterministic and stochastic contributions based on a Langevin equation. The functional form of this equation is directly extracted from experimental data by angle-resolved conditional averages. It contains quadratic 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. Thus our model captures well the dynamics of chemotactic cells and can serve to quantify differences and similarities of different chemotactic eukaryotes. Finally, on the basis of our model, we can characterize the heterogeneity within a population of chemotactic cells.
The chemotaxis of eukaryotic cells depends both on the average concentration of the chemoattractant and on the steepness of its gradient. For the social amoeba Dictyostelium discoideum, we test quantitatively the prediction by Ueda and Shibata [Biophys. J. 93, 11 (2007)] that the efficacy of chemotaxis depends on a single control parameter only, namely, the signal-to-noise ratio (SNR), determined by the stochastic fluctuations of (i) the binding of the chemoattractant molecule to the transmembrane receptor and (ii) the intracellular activation of the effector of the signaling cascade. For SNR less than or similar to 1, the theory captures the experimental findings well, while for larger SNR noise sources further downstream in the signaling pathway need to be taken into account.