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Dynamic processes in living cells are highly organized in space and time. Unraveling the underlying molecular mechanisms of spatiotemporal pattern formation remains one of the outstanding challenges at the interface between physics and biology. A fundamental recurrent pattern found in many different cell types is that of self-sustained oscillations. They are involved in a wide range of cellular functions, including second messenger signaling, gene expression, and cytoskeletal dynamics. Here, we review recent developments in the field of cellular oscillations and focus on cases where concepts from physics have been instrumental for understanding the underlying mechanisms. We consider biochemical and genetic oscillators as well as oscillations that arise from chemo-mechanical coupling. Finally, we highlight recent studies of intracellular waves that have increasingly moved into the focus of this research field.
Flow control is a highly relevant topic for micromanipulation of colloidal particles in microfluidic applications. Here, we report on a system that combines two-surface bound flows emanating from thermo-osmotic and diffusio-osmotic mechanisms. These opposing flows are generated at a gold surface immersed into an aqueous solution containing a photo-sensitive surfactant, which is irradiated by a focused UV laser beam. At low power of incoming light, diffusio-osmotic flow due to local photo-isomerization of the surfactant dominates, resulting in a flow pattern oriented away from the irradiated area. In contrast, thermo-osmotic flow takes over due to local heating of the gold surface at larger power, consequently inducing a flow pointing toward the hotspot. In this way, this system allows one to reversibly switch from outward to inward liquid flow with an intermittent range of zero flow at which tracer particles undergo thermal motion by just tuning the laser intensity only. Our work, thus, demonstrates an optofluidic system for flow generation with a high degree of controllability that is necessary to transport particles precisely to desired locations, thereby opening innovative possibilities to generate advanced microfluidic applications.
Bacterial chemotaxis-a fundamental example of directional navigation in the living world-is key to many biological processes, including the spreading of bacterial infections. Many bacterial species were recently reported to exhibit several distinct swimming modes-the flagella may, for example, push the cell body or wrap around it. How do the different run modes shape the chemotaxis strategy of a multimode swimmer? Here, we investigate chemotactic motion of the soil bacterium Pseudomonas putida as a model organism. By simultaneously tracking the position of the cell body and the configuration of its flagella, we demonstrate that individual run modes show different chemotactic responses in nutrition gradients and, thus, constitute distinct behavioral states. On the basis of an active particle model, we demonstrate that switching between multiple run states that differ in their speed and responsiveness provides the basis for robust and efficient chemotaxis in complex natural habitats.
Chemotactic motion in a chemical gradient is an essential cellular function that controls many processes in the living world. For a better understanding and more detailed modelling of the underlying mechanisms of chemotaxis, quantitative investigations in controlled environments are needed. We developed a setup that allows us to separately address the dependencies of the chemotactic motion on the average background concentration and on the gradient steepness of the chemoattractant. In particular, both the background concentration and the gradient steepness can be kept constant at the position of the cell while it moves along in the gradient direction. This is achieved by generating a well-defined chemoattractant gradient using flow photolysis. In this approach, the chemoattractant is released by a light-induced reaction from a caged precursor in a microfluidic flow chamber upstream of the cell. The flow photolysis approach is combined with an automated real-time cell tracker that determines changes in the cell position and triggers movement of the microscope stage such that the cell motion is compensated and the cell remains at the same position in the gradient profile. The gradient profile can be either determined experimentally using a caged fluorescent dye or may be alternatively determined by numerical solutions of the corresponding physical model. To demonstrate the function of this adaptive microfluidic gradient generator, we compare the chemotactic motion of Dictyostelium discoideum cells in a static gradient and in a gradient that adapts to the position of the moving cell. Published by AIP Publishing.
Bacteria swim in sequences of straight runs that are interrupted by turning events. They drive their swimming locomotion with the help of rotating helical flagella. Depending on the number of flagella and their arrangement across the cell body, different run-and-turn patterns can be observed. Here, we present fluorescence microscopy recordings showing that cells of the soil bacterium Pseudomonas putida that are decorated with a polar tuft of helical flagella, can alternate between two distinct swimming patterns. On the one hand, they can undergo a classical push-pull-push cycle that is well known from monopolarly flagellated bacteria but has not been reported for species with a polar bundle of multiple flagella. Alternatively, upon leaving the pulling mode, they can enter a third slow swimming phase, where they propel themselves with their helical bundle wrapped around the cell body. A theoretical estimate based on a random-walk model shows that the spreading of a population of swimmers is strongly enhanced when cycling through a sequence of pushing, pulling, and wrapped flagellar configurations as compared to the simple push-pull-push pattern.
Modeling random crawling, membrane deformation and intracellular polarity of motile amoeboid cells
(2018)
Amoeboid movement is one of the most widespread forms of cell motility that plays a key role in numerous biological contexts. While many aspects of this process are well investigated, the large cell-to-cell variability in the motile characteristics of an otherwise uniform population remains an open question that was largely ignored by previous models. In this article, we present a mathematical model of amoeboid motility that combines noisy bistable kinetics with a dynamic phase field for the cell shape. To capture cell-to-cell variability, we introduce a single parameter for tuning the balance between polarity formation and intracellular noise. We compare numerical simulations of our model to experiments with the social amoeba Dictyostelium discoideum. Despite the simple structure of our model, we found close agreement with the experimental results for the center-of-mass motion as well as for the evolution of the cell shape and the overall intracellular patterns. We thus conjecture that the building blocks of our model capture essential features of amoeboid motility and may serve as a starting point for more detailed descriptions of cell motion in chemical gradients and confined environments.
What is the underlying diffusion process governing the spreading dynamics and search strategies employed by amoeboid cells? Based on the statistical analysis of experimental single-cell tracking data of the two-dimensional motion of the Dictyostelium discoideum amoeboid cells, we quantify their diffusive behaviour based on a number of standard and complementary statistical indicators. We compute the ensemble- and time-averaged mean-squared displacements (MSDs) of the diffusing amoebae cells and observe a pronounced spread of short-time diffusion coefficients and anomalous MSD-scaling exponents for individual cells. The distribution functions of the cell displacements, the long-tailed distribution of instantaneous speeds, and the velocity autocorrelations are also computed. In particular, we observe a systematic superdiffusive short-time behaviour for the ensemble- and time-averaged MSDs of the amoeboid cells. Also, a clear anti-correlation of scaling exponents and generalised diffusivity values for different cells is detected. Most significantly, we demonstrate that the distribution function of the cell displacements has a strongly non-Gaussian shape andusing a rescaled spatio-temporal variablethe cell-displacement data collapse onto a universal master curve. The current analysis of single-cell motions can be implemented for quantifying diffusive behaviours in other living-matter systems, in particular, when effects of active transport, non-Gaussian displacements, and heterogeneity of the population are involved in the dynamics.
We provide a detailed stochastic description of the swimming motion of an E. coli bacterium in two dimension, where we resolve tumble events in time. For this purpose, we set up two Langevin equations for the orientation angle and speed dynamics. Calculating moments, distribution and autocorrelation functions from both Langevin equations and matching them to the same quantities determined from data recorded in experiments, we infer the swimming parameters of E. coli. They are the tumble rate lambda, the tumble time r(-1), the swimming speed v(0), the strength of speed fluctuations sigma, the relative height of speed jumps eta, the thermal value for the rotational diffusion coefficient D-0, and the enhanced rotational diffusivity during tumbling D-T. Conditioning the observables on the swimming direction relative to the gradient of a chemoattractant, we infer the chemotaxis strategies of E. coli. We confirm the classical strategy of a lower tumble rate for swimming up the gradient but also a smaller mean tumble angle (angle bias). The latter is realized by shorter tumbles as well as a slower diffusive reorientation. We also find that speed fluctuations are increased by about 30% when swimming up the gradient compared to the reversed direction.
The supercritical Hopf bifurcation is one of the simplest ways in which a stationary state of a nonlinear system can undergo a transition to stable self-sustained oscillations. At the bifurcation point, a small-amplitude limit cycle is born, which already at onset displays a finite frequency. If we consider a reaction-diffusion system that undergoes a supercritical Hopf bifurcation, its dynamics is described by the complex Ginzburg-Landau equation (CGLE). Here, we study such a system in the parameter regime where the CGLE shows spatio-temporal chaos. We review a type of time-delay feedback methods which is suitable to suppress chaos and replace it by other spatio-temporal solutions such as uniform oscillations, plane waves, standing waves, and the stationary state.
Cell-driven microtransport is one of the most prominent applications in the emerging field of biohybrid systems. While bacterial cells have been successfully employed to drive the swimming motion of micrometer-sized cargo particles, the transport capacities of motile adherent cells remain largely unexplored. Here, it is demonstrated that motile amoeboid cells can act as efficient and versatile trucks to transport microcargo. When incubated together with microparticles, cells of the social amoeba Dictyostelium discoideum readily pick up and move the cargo particles. Relying on the unspecific adhesive properties of the amoeba, a wide range of different cargo materials can be used. The cell-driven transport can be directionally guided based on the chemotactic responses of amoeba to chemoattractant gradients. On the one hand, the cargo can be assembled into clusters in a self-organized fashion, relying on the developmentally induced chemotactic aggregation of cells. On the other hand, chemoattractant gradients can be externally imposed to guide the cellular microtrucks to a desired location. Finally, larger cargo particles of different shapes that exceed the size of a single cell by more than an order of magnitude, can also be transported by the collective effort of large numbers of motile cells.