Cycling State that Can Lead to Glassy Dynamics in Intracellular Transport

Abstract

Power-law dwell times have been observed for molecular motors in living cells, but the origins of these trapped states are not known. We introduce a minimal model of motors moving on a twodimensional network of filaments, and simulations of its dynamics exhibit statistics comparable to those observed experimentally. Analysis of the model trajectories, as well as experimental particle tracking data, reveals a state in which motors cycle unproductively at junctions of three or more filaments. We formulate a master equation for these junction dynamics and show that the time required to escape from this vortex-like state can account for the power-law dwell times. We identify trends in the dynamics with the motor valency for further experimental validation. We demonstrate that these trends exist in individual trajectories of myosin II on an actin network.We discuss how cells could regulate intracellular transport and, in turn, biological function, by controlling their cytoskeletal network structures locally.Individual microscopic particles (beads [1,2] or fluorescently labeled molecules [3-5]) can now be tracked in cells. These studies reveal complex dynamics [6][7][8]. The resulting trajectories can be treated as random walks, and quantitative analysis of their statistics can provide insights into underlying mechanisms [1,9]. Often, the mean square displacement (MSD) exhibits a power-law (typically sublinear) dependence on the separation in time between two observations, known as the lag time (∆) [1,9,[11][12][13][14][15]. In certain cases [9,14], the MSD also decays as the amount of data included in averages (the measurement time, T ) increases; this trend indicates a power-law distribution of dwell times and is known as "aging" in theories of glassy dynamic [16].These correlations can have important biological implications [9,17]. For example, a recent study shows that the anomalous dynamics observed for insulin secretory vesicles (granules) can account for the biphasic kinetics of insulin release [9] without the need to invoke separate pools of granules, as previously [18]. In particular, the sustained release relies on the glassy dynamics. Glassy dynamics are often interpreted in terms of trapping in local minima of an energy landscape with an exponential or power-law distribution of depths [19,20]. However, how such a landscape could arise from typical biomolecular interactions is unclear. Crowding is insufficient, as it results in standard Brownian motion but with a reduced diffusion coefficient [21]. Because the moving vesicles are associated with molecular motors, which consume cellular energy stores (nucleotide triphosphates) for directed motion along cytoskeletal filaments, other, intrinsically nonequilibrium mechanisms of generating these statistics may exist.In the case of insulin release, the vesicles have both kinesin and dynein associated with them [22], which walk in opposite directions on microtubules [23]. More generally, many cytoskeletal assemblies in cells have multiple motors associated with ...

Nonequilibrium phase diagrams for actomyosin networks

“…Interestingly, in our simulations, the peak at θ = π diminishes at intermediate times. A key feature of the experiments that is not represented in the present model is that the myosin minifilaments in the experiments have many heads 51–53 , and this was previously shown to be important for observed glassy dynamics 53 .…”

Heterogeneities Shape Passive Intracellular Transport

“…Material transport behavior with non-Gaussian increments has been observed and investigated in several non-living complex systems (46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62), ranging from glasses to granular matter and artificial F-actin networks, as well as for the dynamics of receptors on live cell membranes (41) and recently in living prokaryotic cell interiors (63) and in yeast (40). In those systems, non-Gaussian transport features have been rationalized by 1) sample-based variability, 2) rare occurring strong motion events, 3) aging, or 4) spatiotemporal heterogeneities of the medium.…”

Actin bundle architecture and mechanics regulate myosin II force generation

“…1B, white puncta). The number density of myosin puncta on the network is sufficiently low (510 -3 myosin puncta/µm 2 ) that they do not generate enough force to deform the network (33,34).…”

“…Traps are regions of balanced polarity, where myosin has sustained, high forces Decreased myosin puncta speed and trapped periods on mixed polarity bundles could result from multiple physical mechanisms, such as direction switching, tug-of-war, or kinetic trapping (16,34). Using a computational model, we investigate the microscopic mechanisms by which F-actin bundle architecture impacts myosin filament dynamics in silico (26).…”