A stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.

Cytoplasmic transport of organelles, nucleic acids and proteins on microtubules is usually bidirectional with dynein and kinesin motors mediating the delivery of cargoes in the cytoplasm. Here we combine live cell microscopy, single virus tracking and trajectory segmentation to systematically identi...

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Main Authors: Mattia Gazzola, Christoph J Burckhardt, Basil Bayati, Martin Engelke, Urs F Greber, Petros Koumoutsakos
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2009-12-01
Series:PLoS Computational Biology
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20041204/pdf/?tool=EBI
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spelling doaj-bceb26f9e3564446ac4dfcdff125b2462021-04-21T15:08:30ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582009-12-01512e100062310.1371/journal.pcbi.1000623A stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.Mattia GazzolaChristoph J BurckhardtBasil BayatiMartin EngelkeUrs F GreberPetros KoumoutsakosCytoplasmic transport of organelles, nucleic acids and proteins on microtubules is usually bidirectional with dynein and kinesin motors mediating the delivery of cargoes in the cytoplasm. Here we combine live cell microscopy, single virus tracking and trajectory segmentation to systematically identify the parameters of a stochastic computational model of cargo transport by molecular motors on microtubules. The model parameters are identified using an evolutionary optimization algorithm to minimize the Kullback-Leibler divergence between the in silico and the in vivo run length and velocity distributions of the viruses on microtubules. The present stochastic model suggests that bidirectional transport of human adenoviruses can be explained without explicit motor coordination. The model enables the prediction of the number of motors active on the viral cargo during microtubule-dependent motions as well as the number of motor binding sites, with the protein hexon as the binding site for the motors.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20041204/pdf/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Mattia Gazzola
Christoph J Burckhardt
Basil Bayati
Martin Engelke
Urs F Greber
Petros Koumoutsakos
spellingShingle Mattia Gazzola
Christoph J Burckhardt
Basil Bayati
Martin Engelke
Urs F Greber
Petros Koumoutsakos
A stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.
PLoS Computational Biology
author_facet Mattia Gazzola
Christoph J Burckhardt
Basil Bayati
Martin Engelke
Urs F Greber
Petros Koumoutsakos
author_sort Mattia Gazzola
title A stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.
title_short A stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.
title_full A stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.
title_fullStr A stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.
title_full_unstemmed A stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.
title_sort stochastic model for microtubule motors describes the in vivo cytoplasmic transport of human adenovirus.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2009-12-01
description Cytoplasmic transport of organelles, nucleic acids and proteins on microtubules is usually bidirectional with dynein and kinesin motors mediating the delivery of cargoes in the cytoplasm. Here we combine live cell microscopy, single virus tracking and trajectory segmentation to systematically identify the parameters of a stochastic computational model of cargo transport by molecular motors on microtubules. The model parameters are identified using an evolutionary optimization algorithm to minimize the Kullback-Leibler divergence between the in silico and the in vivo run length and velocity distributions of the viruses on microtubules. The present stochastic model suggests that bidirectional transport of human adenoviruses can be explained without explicit motor coordination. The model enables the prediction of the number of motors active on the viral cargo during microtubule-dependent motions as well as the number of motor binding sites, with the protein hexon as the binding site for the motors.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20041204/pdf/?tool=EBI
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