Fluctuating, Lorentz-force-like coupling of Langevin equations and heat flux rectification

In a description of physical systems with Langevin equations, interacting degrees of freedom are usually coupled through symmetric parameter matrices. This coupling symmetry is a consequence of time-reversal symmetry of the involved conservative forces. If coupling parameters fluctuate randomly, the resulting noise is called multiplicative. For example, mechanical oscillators can be coupled through a fluctuating, symmetric matrix of spring "constants". Such systems exhibit well-studied instabilities. In this note, we study the complementary case of antisymmetric, time-reversal symmetry breaking coupling that can be realized with Lorentz forces or various gyrators. We consider the case that these antisymmetric couplings fluctuate. This type of multiplicative noise does not lead to instabilities in the stationary state but renormalizes the effective non-equilibrium friction. Fluctuating Lorentz-force-like couplings also allow to control and rectify heat transfer. A noteworthy property of this mechanism of producing asymmetric heat flux is that the controlling couplings do not exchange energy with the system.

Fluctuating, Lorentz-force-like coupling of Langevin equations and heat flux rectification

In a description of physical systems with Langevin equations, interacting degrees of freedom are usually coupled through symmetric parameter matrices. This coupling symmetry is a consequence of time-reversal symmetry of the involved conservative forces. If coupling parameters fluctuate randomly, the resulting noise is called multiplicative. For example, mechanical oscillators can be coupled through a fluctuating, symmetric matrix of spring "constants". Such systems exhibit well-studied instabilities. In this note, we study the complementary case of antisymmetric, time-reversal symmetry breaking coupling that can be realized with Lorentz forces or various gyrators. We consider the case that these antisymmetric couplings fluctuate. This type of multiplicative noise does not lead to instabilities in the stationary state but renormalizes the effective non-equilibrium friction. Fluctuating Lorentz-force-like couplings also allow to control and rectify heat transfer. A noteworthy property of this mechanism of producing asymmetric heat flux is that the controlling couplings do not exchange energy with the system.

Nonlinear, electrocatalytic swimming in the presence of salt

A small, bimetallic particle in a hydrogen peroxide solution can propel itself by means of an electrocatalytic reaction. The swimming is driven by a flux of ions around the particle. We model this process for the presence of a monovalent salt, where reaction-driven proton currents induce salt ion currents. A theory for thin diffuse layers is employed, which yields nonlinear, coupled transport equations. The boundary conditions include a compact Stern layer of adsorbed ions. Electrochemical processes on the particle surface are modeled with a first order reaction of the Butler-Volmer type. The equations are solved numerically for the swimming speed. An analytical approximation is derived under the assumption that the decomposition of hydrogen peroxide occurs mainly without inducing an electric current. We find that the swimming speed increases linearly with hydrogen peroxide concentration for small concentrations. The influence of ion diffusion on the reaction rate can lead to a concave shape of the function of speed vs. hydrogen peroxide concentration. The compact layer of ions on the particle diminishes the reaction rate and consequently reduces the speed. Our results are consistent with published experimental data

Role of the membrane for mechanosensing by tethered channels

Biologically important membrane channels are gated by force at attached tethers. Here, we generically characterize the non-trivial interplay of force, membrane tension, and channel deformations that can affect gating. A central finding is that minute conical channel deformation under force leads to significant energy release during opening. We also calculate channel-channel interactions and show that they can amplify force sensitivity of tethered channels

Efficiency of surface-driven motion: nano-swimmers beat micro-swimmers

Surface interactions provide a class of mechanisms which can be employed for propulsion of micro- and nanometer sized particles. We investigate the related efficiency of externally and self-propelled swimmers. A general scaling relation is derived showing that only swimmers whose size is comparable to, or smaller than, the interaction range can have appreciable efficiency. An upper bound for efficiency at maximum power is 1/2. Numerical calculations for the case of diffusiophoresis are found to be in good agreement with analytical expressions for the efficiency

Collective force generation by groups of migrating bacteria

From biofilm and colony formation in bacteria to wound healing and embryonic development in multicellular organisms, groups of living cells must often move collectively. While considerable study has probed the biophysical mechanisms of how eukaryotic cells generate forces during migration, little such study has been devoted to bacteria, in particular with regard to the question of how bacteria generate and coordinate forces during collective motion. This question is addressed here for the first time using traction force microscopy. We study two distinct motility mechanisms of Myxococcus xanthus, namely twitching and gliding. For twitching, powered by type-IV pilus retraction, we find that individual cells exert local traction in small hotspots with forces on the order of 50 pN. Twitching of bacterial groups also produces traction hotspots, however with amplified forces around 100 pN. Although twitching groups migrate slowly as a whole, traction fluctuates rapidly on timescales <1.5 min. Gliding, the second motility mechanism, is driven by lateral transport of substrate adhesions. When cells are isolated, gliding produces low average traction on the order of 1 Pa. However, traction is amplified in groups by a factor of ~5. Since advancing protrusions of gliding cells push on average in the direction of motion, we infer a long-range compressive load sharing among sub-leading cells. Together, these results show that the forces generated during twitching and gliding have complementary characters and both forces are collectively amplified in groups

Dynamics and efficiency of a self-propelled, diffusiophoretic swimmer

Active diffusiophoresis - swimming through interaction with a self-generated, neutral, solute gradient - is a paradigm for autonomous motion at the micrometer scale. We study this propulsion mechanism within a linear response theory. Firstly, we consider several aspects relating to the dynamics of the swimming particle. We extend established analytical formulae to describe small swimmers, which interact with their environment on a finite lengthscale. Solute convection is also taken into account. Modeling of the chemical reaction reveals a coupling between the angular distribution of reactivity on the swimmer and the concentration field. This effect, which we term "reaction induced concentration distortion", strongly influences the particle speed. Building on these insights, we employ irreversible, linear thermodynamics to formulate an energy balance. This approach highlights the importance of solute convection for a consistent treatment of the energetics. The efficiency of swimming is calculated numerically and approximated analytically. Finally, we define an efficiency of transport for swimmers which are moving in random directions. It is shown that this efficiency scales as the inverse of the macroscopic distance over which transport is to occur.Comment: 16 pages, 11 figure

Sensory adaptation in a continuum model of bacterial chemotaxis—working range, cost-accuracy relation, and coupled systems

Sensory adaptation enables organisms to adjust their perception in a changing environment. A paradigm is bacterial chemotaxis, where the output activity of chemoreceptors is adapted to different baseline concentrations via receptor methylation. The range of internal receptor states limits the stimulus magnitude to which these systems can adapt. Here, we employ a highly idealized, Langevin-equation based model to study how the finite range of state variables affects the adaptation accuracy and the energy dissipation in individual and coupled systems. Maintaining an adaptive state requires constant energy dissipation. We show that the steady-state dissipation rate increases approximately linearly with the adaptation accuracy for varying stimulus magnitudes in the so-called perfect adaptation limit. This result complements the well-known logarithmic cost-accuracy relationship for varying chemical driving. Next, we study linearly coupled pairs of sensory units. We find that the interaction reduces the dissipation rate per unit and affects the overall cost-accuracy relationship. A coupling of the slow methylation variables results in a better accuracy than a coupling of activities. Overall, the findings highlight the significance of both the working range and collective operation mode as crucial design factors that impact the accuracy and energy expenditure of molecular adaptation networks

Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells

Adherent cells exert traction forces on to their environment, which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures. This traction can be measured in a perturbation-free manner with traction force microscopy (TFM). In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular traction forces