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Spatial Modeling of Vesicle Transport and the Cytoskeleton: The Challenge of Hitting the Right Road

Overview of attention for article published in PLOS ONE, January 2012
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Title
Spatial Modeling of Vesicle Transport and the Cytoskeleton: The Challenge of Hitting the Right Road
Published in
PLOS ONE, January 2012
DOI 10.1371/journal.pone.0029645
Pubmed ID
Authors

Michael Klann, Heinz Koeppl, Matthias Reuss

Abstract

The membrane trafficking machinery provides a transport and sorting system for many cellular proteins. We propose a mechanistic agent-based computer simulation to integrate and test the hypothesis of vesicle transport embedded into a detailed model cell. The method tracks both the number and location of the vesicles. Thus both the stochastic properties due to the low numbers and the spatial aspects are preserved. The underlying molecular interactions that control the vesicle actions are included in a multi-scale manner based on the model of Heinrich and Rapoport (2005). By adding motor proteins we can improve the recycling process of SNAREs and model cell polarization. Our model also predicts that coat molecules should have a high turnover at the compartment membranes, while the turnover of motor proteins has to be slow. The modular structure of the underlying model keeps it tractable despite the overall complexity of the vesicle system. We apply our model to receptor-mediated endocytosis and show how a polarized cytoskeleton structure leads to polarized distributions in the plasma membrane both of SNAREs and the Ste2p receptor in yeast. In addition, we can couple signal transduction and membrane trafficking steps in one simulation, which enables analyzing the effect of receptor-mediated endocytosis on signaling.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Spain 1 1%
Ireland 1 1%
France 1 1%
Unknown 81 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 35%
Researcher 15 17%
Student > Master 8 9%
Professor > Associate Professor 6 7%
Student > Bachelor 5 6%
Other 13 15%
Unknown 9 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 35%
Biochemistry, Genetics and Molecular Biology 12 14%
Physics and Astronomy 11 13%
Engineering 6 7%
Computer Science 4 5%
Other 14 16%
Unknown 9 10%