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Modeling Recursive RNA Interference

Overview of attention for article published in PLoS Computational Biology, September 2008
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Title
Modeling Recursive RNA Interference
Published in
PLoS Computational Biology, September 2008
DOI 10.1371/journal.pcbi.1000183
Pubmed ID
Authors

Wallace F. Marshall

Abstract

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off-switch on experiments.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 6%
Germany 2 4%
Italy 1 2%
Brazil 1 2%
United States 1 2%
Luxembourg 1 2%
Poland 1 2%
Unknown 38 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 35%
Student > Ph. D. Student 13 27%
Professor 4 8%
Student > Doctoral Student 3 6%
Professor > Associate Professor 3 6%
Other 6 13%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 58%
Physics and Astronomy 4 8%
Biochemistry, Genetics and Molecular Biology 3 6%
Computer Science 3 6%
Engineering 2 4%
Other 6 13%
Unknown 2 4%