Tracking of plus-ends reveals microtubule functional diversity in different cell types

Kinematics of persistent random walkers with two distinct modes of motion

We study the stochastic motion of active particles that undergo spontaneous transitions between two distinct modes of motion. Each mode is characterized by a velocity distribution and an arbitrary (anti)persistence. We present an analytical formalism to provide a quantitative link between these two microscopic statistical properties of the trajectory and macroscopically observable transport quantities of interest. For exponentially distributed residence times in each state, we derive analytical expressions for the initial anomalous exponent, the characteristic crossover time to the asymptotic diffusive dynamics, and the long-term diffusion constant. We also obtain an exact expression for the time evolution of the mean square displacement over all timescales and provide a recipe to obtain higher displacement moments. Our approach enables us to disentangle the combined effects of velocity, persistence, and switching probabilities between the two states on the kinematics of particles in a wide range of stochastic active or passive processes and to optimize the transport quantities of interest with respect to any of the particle dynamics properties.

Orientational memory of active particles in multistate non-Markovian processes

The orientational memory of particles can serve as an effective measure of diffusivity, spreading, and search efficiency in complex stochastic processes. We develop a theoretical framework to describe the decay of directional correlations in a generic class of stochastic active processes consisting of distinct states of motion characterized by their persistence and switching probabilities between the states. For exponentially distributed sojourn times, the orientation autocorrelation is analytically derived and the characteristic times of its crossovers are obtained in terms of the persistence of each state and the switching probabilities. We show how nonexponential sojourn-time distributions of interest, such as Gaussian and power-law distributions, can result from history-dependent transitions between the states. The relaxation behavior of the correlation function in such non-Markovian processes is governed by the history dependence of the switching probabilities and cannot be solely determined by the mean sojourn times of the states.

Robustness of the microtubule network self-organization in epithelia

Robustness of biological systems is crucial for their survival, however, for many systems its origin is an open question. Here, we analyze one subcellular level system, the microtubule cytoskeleton. Microtubules self-organize into a network, along which cellular components are delivered to their biologically relevant locations. While the dynamics of individual microtubules is sensitive to the organism’s environment and genetics, a similar sensitivity of the overall network would result in pathologies. Our large-scale stochastic simulations show that the self-organization of microtubule networks is robust in a wide parameter range in individual cells. We confirm this robustness in vivo on the tissue-scale using genetic manipulations of Drosophila epithelial cells. Finally, our minimal mathematical model shows that the origin of robustness is the separation of time-scales in microtubule dynamics rates. Altogether, we demonstrate that the tissue-scale self-organization of a microtubule network depends only on cell geometry and the distribution of the microtubule minus-ends.

Microtubule Growth Rates Are Sensitive to Global and Local Changes in Microtubule Plus-End Density

The microtubule cytoskeleton plays critically important roles in numerous cellular functions in eukaryotes, and it does so across a functionally diverse and morphologically disparate range of cell types [1]. In these roles, microtubule assemblies must adopt distinct morphologies and physical dimensions to perform specific functions [2-5]. As such, these macromolecular assemblies-as well as the dynamics of the individual microtubule polymers from which they are made-must scale and change in accordance with cell size, geometry, and function. Microtubules in cells typically assemble to a steady state in mass, leaving enough of their tubulin subunits soluble to allow rapid growth and turnover. This suggests some negative feedback that limits the extent of assembly, for example, decrease in growth rate, or increase in catastrophe rate, as the soluble subunit pool decreases. Although these ideas have informed the field for decades, they have not been observed experimentally. Here, we describe the application of an experimental approach that combines cell-free extracts with photo-patterned hydrogel micro-enclosures as a means to investigate microtubule dynamics in cytoplasmic volumes of defined size and shape. Our measurements reveal a negative correlation between microtubule plus-end density and microtubule growth rates and suggest that these rates are sensitive to the presence of nearby growing ends.

Self-Organized Lane Formation in Bidirectional Transport by Molecular Motors

Within cells, vesicles and proteins are actively transported several micrometers along the cytoskeletal filaments. The transport along microtubules is propelled by dynein and kinesin motors, which carry the cargo in opposite directions. Bidirectional intracellular transport is performed with great efficiency, even under strong confinement, as for example in the axon. For this kind of transport system, one would expect generically cluster formation. In this Letter, we discuss the effect of the recently observed self-enhanced binding affinity along the kinesin trajectories on the microtubule. We introduce a stochastic lattice-gas model, where the enhanced binding affinity is realized via a floor field. From Monte Carlo simulations and a mean-field analysis we show that this mechanism can lead to self-organized symmetry breaking and lane formation that indeed leads to efficient bidirectional transport in narrow environments.

Run-and-pause dynamics of cytoskeletal motor proteins

Cytoskeletal motor proteins are involved in major intracellular transport processes which are vital for maintaining appropriate cellular function. When attached to cytoskeletal filaments, the motor exhibits distinct states of motility: active motion along the filaments, and pause phase in which it remains stationary for a finite time interval. The transition probabilities between motion and pause phases are asymmetric in general, and considerably affected by changes in environmental conditions which influences the efficiency of cargo delivery to specific targets. By considering the motion of individual non-interacting molecular motors on a single filament as well as a dynamic filamentous network, we present an analytical model for the dynamics of self-propelled particles which undergo frequent pause phases. The interplay between motor processivity, structural properties of filamentous network, and transition probabilities between the two states of motility drastically changes the dynamics: multiple transitions between different types of anomalous diffusive dynamics occur and the crossover time to the asymptotic diffusive or ballistic motion varies by several orders of magnitude. We map out the phase diagrams in the space of transition probabilities, and address the role of initial conditions of motion on the resulting dynamics.