Cell migration, which is central to many biological processes including wound healing and cancer progression, is sensitive to environmental stiffness, and many cell types exhibit a stiffness optimum, at which migration is maximal. Here we present a cell migration simulator that predicts a stiffness optimum that can be shifted by altering the number of active molecular motors and clutches. This prediction is verified experimentally by comparing cell traction and F-actin retrograde flow for two cell types with differing amounts of active motors and clutches: embryonic chick forebrain neurons (ECFNs; optimum ∼1 kPa) and U251 glioma cells (optimum ∼100 kPa). In addition, the model predicts, and experiments confirm, that the stiffness optimum of U251 glioma cell migration, morphology and F-actin retrograde flow rate can be shifted to lower stiffness by simultaneous drug inhibition of myosin II motors and integrin-mediated adhesions.

Class XI myosins are plant specific and responsible for cytoplasmic streaming. Because of the large number of myosin XI genes in angiosperms, it has been difficult to determine their precise role, particularly with respect to tip growth. The moss Physcomitrella patens provides an ideal system to study myosin XI function. P. patens has only two myosin XI genes, and these genes encode proteins that are 94% identical to each other. To determine their role in tip growth, we used RNA interference to specifically silence each myosin XI gene using 59 untranslated region sequences. We discovered that the two myosin XI genes are functionally redundant, since silencing of either gene does not affect growth or polarity. However, simultaneous silencing of both myosin XIs results in severely stunted plants composed of small rounded cells. Although similar to the phenotype resulting from silencing of other actin-associated proteins, we show that this phenotype is not due to altered actin dynamics. Consistent with a role in tip growth, we show that a functional, full-length fusion of monomeric enhanced green fluorescent protein (mEGFP) to myosin XI accumulates at a subcortical, apical region of actively growing protonemal cells.

Intracellular cargo transport frequently involves multiple motor types, either having opposite directionality or having the same directionality but different speeds. Although significant progress has been made in characterizing kinesin motors at the single-molecule level, predicting their ensemble behavior is challenging and requires tight coupling between experiments and modeling to uncover the underlying motor behavior. To understand how diverse kinesins attached to the same cargo coordinate their movement, we carried out microtubule gliding assays using pairwise mixtures of motors from the kinesin-1, -2, -3, -5, and -7 families engineered to have identical run lengths and surface attachments. Uniform motor densities were used and microtubule gliding speeds were measured for varying proportions of fast and slow motors. A coarse-grained computational model of gliding assays was developed and found to recapitulate the experiments. Simulations incorporated published force-dependent velocities and run lengths, along with mechanical interactions between motors bound to the same microtubule. The simulations show that the force-dependence of detachment is the key parameter that determines gliding speed in multimotor assays, while motor compliance, surface density, and stall force all play minimal roles. Simulations also provide estimates for force-dependent dissociation rates, suggesting that kinesin-1 and the mitotic motors kinesin-5 and -7 maintain microtubule association against loads, whereas kinesin-2 and -3 readily detach. This work uncovers unexpected motor behavior in multimotor ensembles and clarifies functional differences between kinesins that carry out distinct mechanical tasks in cells.

A recently introduced particle-based model for fluid flow, called stochastic rotation dynamics, can be made Galilean invariant by introducing a random shift of the computational grid before collisions. In this paper, it is shown how the Green-Kubo relations derived previously can be resummed to obtain exact expressions for the collisional contributions to the transport coefficients. It is also shown that the collisional contribution to the microscopic stress tensor is not symmetric, and that this leads to an additional viscosity. The resulting identification of the transport coefficients for the hydrodynamic modes is discussed in detail, and it is shown that this does not impose restrictions on the applicability of the model. The collisional contribution to the thermal conductivity, which becomes important for small mean free path and small average particle number per cell, is also derived.

The dynamic structure factor, vorticity and entropy density dynamic correlation functions are measured for Stochastic Rotation Dynamics (SRD), a particle based algorithm for fluctuating fluids. This allows us to obtain unbiased values for the longitudinal transport coefficients such as thermal diffusivity and bulk viscosity. The results are in good agreement with earlier numerical and theoretical results, and it is shown for the first time that the bulk viscosity is indeed zero for this algorithm. In addition, corrections to the self-diffusion coefficient and shear viscosity arising from the breakdown of the molecular chaos approximation at small mean free paths are analyzed. In addition to deriving the form of the leading correlation corrections to these transport coefficients, the probabilities that two and three particles remain collision partners for consecutive time steps are derived analytically in the limit of small mean free path. The results of this paper verify that we have an excellent understanding of the SRD algorithm at the kinetic level and that analytic expressions for the transport coefficients derived elsewhere do indeed provide a very accurate description of the SRD fluid.