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Computational Analysis of Rho GTPase Cycling

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
Computational Analysis of Rho GTPase Cycling
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002831
Pubmed ID
Authors

Cibele Vieira Falkenberg, Leslie M. Loew

Abstract

The Rho family of GTPases control actin organization during diverse cellular responses (migration, cytokinesis and endocytosis). Although the primary members of this family (RhoA, Rac and Cdc42) have different downstream effects on actin remodeling, the basic mechanism involves targeting to the plasma membrane and activation by GTP binding. Our hypothesis is that the details of GTPase cycling between membrane and cytosol are key to the differential upstream regulation of these biochemical switches. Accordingly, we developed a modeling framework to analyze experimental data for these systems. This analysis can reveal details of GDI-mediated cycling and help distinguish between GDI-dependent and -independent mechanisms, including vesicle trafficking and direct association-dissociation of GTPase with membrane molecules. Analysis of experimental data for Rac membrane cycling reveals that the lower apparent affinity of GDI for RacGTP compared to RacGDP can be fully explained by the faster dissociation of the latter from the membrane. Non-dimensional steady-state solutions for membrane fraction of GTPase are presented in multidimensional charts. This methodology is then used to analyze glucose stimulated Rac cycling in pancreatic β-cells. The charts are used to illustrate the effects of GEFs/GAPs and regulated affinities between GTPases and membrane and/or GDI on the amount of membrane bound GTPase. In a similar fashion, the charts can be used as a guide in assessing how targeted modifications may compensate for altered GTPase-GDI balance in disease scenarios.

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Geographical breakdown

Country Count As %
United States 3 6%
Germany 2 4%
Unknown 45 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 34%
Student > Ph. D. Student 17 34%
Student > Master 4 8%
Student > Bachelor 4 8%
Other 2 4%
Other 3 6%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 44%
Biochemistry, Genetics and Molecular Biology 8 16%
Engineering 6 12%
Mathematics 3 6%
Computer Science 2 4%
Other 3 6%
Unknown 6 12%