Searcharxiv⌕ Search

arXiv · physics/0612054

Intra-cellular transport by single-headed kinesin KIF1A: effects of single-motor mechano-chemistry and steric interactions

Abstract

In eukaryotic cells, many motor proteins can move simultaneously on a single microtubule track. This leads to interesting collective phenomena like jamming. Recently we reported ({\it Phys. Rev. Lett. {\bf 95}, 118101 (2005)}) a lattice-gas model which describes traffic of unconventional (single-headed) kinesins KIF1A. Here we generalize this model, introducing a novel interaction parameter $c$, to account for an interesting mechano-chemical process which has not been considered in any earlier model. We have been able to extract all the parameters of the model, except $c$, from experimentally measured quantities. In contrast to earlier models of intra-cellular molecular motor traffic, our model assigns distinct ``chemical'' (or, conformational) states to each kinesin to account for the hydrolysis of ATP, the chemical fuel of the motor. Our model makes experimentally testable theoretical predictions. We determine the phase diagram of the model in planes spanned by experimentally controllable parameters, namely, the concentrations of kinesins and ATP. Furthermore, the phase-separated regime is studied in some detail using analytical methods and simulations to determine e.g. the position of shocks. Comparison of our theoretical predictions with experimental results is expected to elucidate the nature of the mechano-chemical process captured by the parameter $c$.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Philip Greulich, Ashok Garai, Katsuhiro Nishinari, Andreas Schadschneider, Debashish Chowdhury. 2007-02-17. Intra-cellular transport by single-headed kinesin KIF1A: effects of single-motor mechano-chemistry and steric interactions. https://doi.org/10.1103/physreve.75.041905

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Exploring the World of Lipids with Machine-Learned Coarse-Grained Models

Lipid bilayers are central to cellular organization and function, yet their atomistic modelling remains constrained by its high computational cost. While coarse-grained (CG) models can overcome this bottleneck, they are limited in accuracy due to the use of simplified functional forms based on molecular mechanics. Using a hybrid functional form combining physics-based terms with a graph neural network, we present implicit-solvent, machine-learned CG models for POPC. When trained on single-lipid data, the model recovers the atomistic conformational landscape. By adding short atomistic simulations of a small flat bilayer to the training data and combining force matching with a denoising objective, we train a model that accurately reproduces bilayer structure and dynamics, in closer agreement with the atomistic reference than existing models, like Martini3 and DryMartini. Beyond its training data, the model stabilizes curved geometries, including vesicles, and captures complex cooperative phenomena such as bilayer and vesicle self-assembly and pore sealing. While limitations remain, particularly regarding chemical transferability, our results establish machine-learned coarse-grained potentials as a promising path towards robust and accurate CG models of lipids and membranes.

physics.bio-ph↗

Mechanical properties of chiral helical polymers-from single filaments to actin bundles

The mechanical properties of biological polymers are essential to their biological functions. Here, we introduce a highly coarse-grained model of helical filaments that preserves helicity and chirality while enabling mesoscale simulations. This is implemented in Cytosim, a widely-used open-source cytoskeleton simulation platform. We first show that we can predict the shape and mechanical properties of this helical filament, allowing for its fine control. We find the parameters that allow the resulting filament to accurately match the properties of actin filaments found experimentally}. Using this model of actin, we investigated the role of actin chirality in motor-driven dynamics. We first show that in two different experimental configurations, motor movement along a helical filament results in a chiral motion of the filament. In a bundle of helical filaments, dimeric motors exert torques on each filament, inducing collective behaviors in the bundle such as rotation, coiling, and helical buckling, reminiscent of those observed in filopodia. Together, these results demonstrate the central role of helicity and chirality in actin mechanics and motor-driven dynamics, and establish our framework as a powerful tool for mesoscale simulations. This framework can also be used for other helical filaments beyond actin.

physics.bio-ph↗

Finite-size scaling and cutoff functions of critical avalanches under cubic saturation

Power-law avalanche statistics characterize the branching bulk but leave open how finite systems terminate their largest events. Here we determine both the finite-size cutoff and the full crossover function connecting the branching regime to the large-event tail for an exchangeable absorbing event process motivated by Cauchy-stable recurrent dynamics. The Cauchy dynamical mean-field theory yields a linear-cubic low-activity drift, while a repeatable-event lift supplies absorbing branching fluctuations. Their competition at criticality generates a distinct finite-size edge controlled by cubic saturation. For avalanches initiated by a fixed microscopic seed, the rescaled edge measure connects the universal branching power law continuously to an exponential large-area tail. This full crossover admits an analytic spectral representation in terms of the boundary spectrum of a quartic Schrödinger operator on the half-line: high modes reconstruct the branching singularity, while the ground state fixes the far-tail decay. Exact finite-size calculations and Monte Carlo simulations confirm both the predicted cutoff behavior and the full spectral crossover.

physics.bio-ph↗