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Auteurs principaux: Toit, Nadine du, Müller-Nedebock, Kristian K.
Format: Preprint
Publié: 2025
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Accès en ligne:https://arxiv.org/abs/2509.03671
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author Toit, Nadine du
Müller-Nedebock, Kristian K.
author_facet Toit, Nadine du
Müller-Nedebock, Kristian K.
contents This paper builds on a recently introduced dynamical networking framework, applying it to model motor-driven transport along cytoskeletal filament networks. Within this approach, the networking functional describes the periodic binding and unbinding of motors to available filament sites,whilst accounting for all possible pairing, enabling a field-theoretic treatment of constrained motion in complex networks. In this application, the dynamical networking theory is introduced into a Martin-Siggia-Rose representation of the Langevin dynamics describing the motion of a motor protein and its cargo. Results are presented in a collective description of motors on a network, for two different scenarios, namely homogeneous and non-homogeneous networks. A diffusion coefficient is presented for homogeneous networks, whilst it is shown that various possibilities remain for disordered averaging over network densities for non-homogeneous networks.
format Preprint
id arxiv_https___arxiv_org_abs_2509_03671
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Applying a Gaussian networking theory to model motor-driven transport along cytoskeletal filaments
Toit, Nadine du
Müller-Nedebock, Kristian K.
Soft Condensed Matter
Biological Physics
This paper builds on a recently introduced dynamical networking framework, applying it to model motor-driven transport along cytoskeletal filament networks. Within this approach, the networking functional describes the periodic binding and unbinding of motors to available filament sites,whilst accounting for all possible pairing, enabling a field-theoretic treatment of constrained motion in complex networks. In this application, the dynamical networking theory is introduced into a Martin-Siggia-Rose representation of the Langevin dynamics describing the motion of a motor protein and its cargo. Results are presented in a collective description of motors on a network, for two different scenarios, namely homogeneous and non-homogeneous networks. A diffusion coefficient is presented for homogeneous networks, whilst it is shown that various possibilities remain for disordered averaging over network densities for non-homogeneous networks.
title Applying a Gaussian networking theory to model motor-driven transport along cytoskeletal filaments
topic Soft Condensed Matter
Biological Physics
url https://arxiv.org/abs/2509.03671