No Arabic abstract
In a classic paper, Edward Purcell analysed the dynamics of flagellated bacterial swimmers and derived a geometrical relationship which optimizes the propulsion efficiency. Experimental measurements for wild-type bacterial species E. coli have revealed that they closely satisfy this geometric optimality. However, the dependence of the flagellar motor speed on the load and more generally the role of the torque-speed characteristics of the flagellar motor is not considered in Purcells original analysis. Here we derive a tuned condition representing a match between the flagella geometry and the torque-speed characteristics of the flagellar motor to maximize the bacterial swimming speed for a given load. This condition is independent of the geometric optimality condition derived by Purcell and interestingly this condition is not satisfied by wild-type E. coli which swim 2-3 times slower than the maximum possible speed given the amount of available motor torque. Our analysis also reveals the existence of an anomalous propulsion regime, where the swim speed increases with increasing load (drag). Finally, we present experimental data which supports our analysis.
The flexibility of the bacterial flagellar hook is believed to have substantial consequences for microorganism locomotion. Using a simplified model of a rigid flagellum and a flexible hook, we show that the paths of axisymmetric cell bodies driven by a single flagellum in Stokes flow are generically helical. Phase-averaged resistance and mobility tensors are produced to describe the flagellar hydrodynamics, and a helical rod model which retains a coupling between translation and rotation is identified as a distinguished asymptotic limit. A supercritical Hopf bifurcation in the flagellar orientation beyond a critical ratio of flagellar motor torque to hook bending stiffness, which is set by the spontaneous curvature of the flexible hook, the shape of the cell body, and the flagellum geometry, can have a dramatic effect on the cells trajectory through the fluid. Although the equilibrium hook angle can result in a wide variance in the trajectorys helical pitch, we find a very consistent prediction for the trajectorys helical amplitude using parameters relevant to swimming P. aeruginosa cells.
Inspired by recent experiments on the effects of cytosolic crowders on the organization of bacterial chromosomes, we consider a feather-boa type model chromosome in the presence of non-additive crowders, encapsulated within a cylindrical cell. We observe spontaneous emergence of complementary helicity of the confined polymer and crowders. This feature is reproduced within a simplified effective model of the chromosome. This latter model further establishes the occurrence of longitudinal and transverse spatial segregation transitions between the chromosome and crowders upon increasing crowder size.
Switching of the direction of flagella rotations is the key control mechanism governing the chemotactic activity of E. coli and many other bacteria. Power-law distributions of switching times are most peculiar because their emergence cannot be deduced from simple thermodynamic arguments. Recently it was suggested that by adding finite-time correlations into Gaussian fluctuations regulating the energy height of barrier between the two rotation states, one can generate a power-law switching statistics. By using a simple model of a regulatory pathway, we demonstrate that the required amount of correlated `noise can be produced by finite number fluctuations of reacting protein molecules, a condition common to the intracellular chemistry. The corresponding power-law exponent appears as a tunable characteristic controlled by parameters of the regulatory pathway network such as equilibrium number of molecules, sensitivities, and the characteristic relaxation time.
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 microaerophilic magnetotactic bacterium Magnetospirillum gryphiswaldense swims along magnetic field lines using a single flagellum at each cell pole. It is believed that this magnetotactic behavior enables cells to seek optimal oxygen concentration with maximal efficiency. We analyse the trajectories of swimming M. gryphiswaldense cells in external magnetic fields larger than the earths field, and show that each cell can switch very rapidly (in < 0.2 s) between a fast and a slow swimming mode. Close to a glass surface, a variety of trajectories was observed, from straight swimming that systematically deviates from field lines to various helices. A model in which fast (slow) swimming is solely due to the rotation of the trailing (leading) flagellum can account for these observations. We determined the magnetic moment of this bacterium using a new method, and obtained a value of (2.0 $pm$ 0.6) $times$ $10^{-16}$ Am$^2$. This value is found to be consistent with parameters emerging from quantitative fitting of trajectories to our model.