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Full Design Automation of Multi-State RNA Devices to Program Gene Expression Using Energy-Based Optimization

Overview of attention for article published in PLoS Computational Biology, August 2013
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Title
Full Design Automation of Multi-State RNA Devices to Program Gene Expression Using Energy-Based Optimization
Published in
PLoS Computational Biology, August 2013
DOI 10.1371/journal.pcbi.1003172
Pubmed ID
Authors

Guillermo Rodrigo, Thomas E. Landrain, Eszter Majer, José-Antonio Daròs, Alfonso Jaramillo

Abstract

Small RNAs (sRNAs) can operate as regulatory agents to control protein expression by interaction with the 5' untranslated region of the mRNA. We have developed a physicochemical framework, relying on base pair interaction energies, to design multi-state sRNA devices by solving an optimization problem with an objective function accounting for the stability of the transition and final intermolecular states. Contrary to the analysis of the reaction kinetics of an ensemble of sRNAs, we solve the inverse problem of finding sequences satisfying targeted reactions. We show here that our objective function correlates well with measured riboregulatory activity of a set of mutants. This has enabled the application of the methodology for an extended design of RNA devices with specified behavior, assuming different molecular interaction models based on Watson-Crick interaction. We designed several YES, NOT, AND, and OR logic gates, including the design of combinatorial riboregulators. In sum, our de novo approach provides a new paradigm in synthetic biology to design molecular interaction mechanisms facilitating future high-throughput functional sRNA design.

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The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
France 1 2%
United Kingdom 1 2%
Belgium 1 2%
Iran, Islamic Republic of 1 2%
Spain 1 2%
China 1 2%
Unknown 53 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 33%
Student > Ph. D. Student 15 25%
Student > Master 9 15%
Student > Bachelor 5 8%
Professor 5 8%
Other 6 10%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 49%
Biochemistry, Genetics and Molecular Biology 12 20%
Computer Science 4 7%
Engineering 3 5%
Chemistry 3 5%
Other 4 7%
Unknown 5 8%