Dataset Information

Stochastic simulation of neurofilament transport in axons: the "stop-and-go" hypothesis.

ABSTRACT: According to the "stop-and-go" hypothesis of slow axonal transport, cytoskeletal and cytosolic proteins are transported along axons at fast rates but the average velocity is slow because the movements are infrequent and bidirectional. To test whether this hypothesis can explain the kinetics of slow axonal transport in vivo, we have developed a stochastic model of neurofilament transport in axons. We propose that neurofilaments move in both anterograde and retrograde directions along cytoskeletal tracks, alternating between short bouts of rapid movement and short "on-track" pauses, and that they can also temporarily disengage from these tracks, resulting in more prolonged "off-track" pauses. We derive the kinetic parameters of the model from a detailed analysis of the moving and pausing behavior of single neurofilaments in axons of cultured neurons. We show that the model can match the shape, velocity, and spreading of the neurofilament transport waves obtained by radioisotopic pulse labeling in vivo. The model predicts that axonal neurofilaments spend approximately 8% of their time on track and approximately 97% of their time pausing during their journey along the axon.

SUBMITTER: Brown A

PROVIDER: S-EPMC1196334 | biostudies-literature | 2005 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Stochastic simulation of neurofilament transport in axons: the "stop-and-go" hypothesis.

Brown Anthony A Wang Lei L Jung Peter P

Molecular biology of the cell 20050706 9

According to the "stop-and-go" hypothesis of slow axonal transport, cytoskeletal and cytosolic proteins are transported along axons at fast rates but the average velocity is slow because the movements are infrequent and bidirectional. To test whether this hypothesis can explain the kinetics of slow axonal transport in vivo, we have developed a stochastic model of neurofilament transport in axons. We propose that neurofilaments move in both anterograde and retrograde directions along cytoskeletal ...[more]

PMID: 16000374

Similar Datasets

Project description:BackgroundExperiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling.ResultsWe have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development.ConclusionsIn this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www.urdme.org.

Dataset Information

Stochastic simulation of neurofilament transport in axons: the "stop-and-go" hypothesis.

Publications

Stochastic simulation of neurofilament transport in axons: the "stop-and-go" hypothesis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets