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Programmable In Vivo Selection of Arbitrary DNA Sequences

Overview of attention for article published in PLOS ONE, November 2012
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Title
Programmable In Vivo Selection of Arbitrary DNA Sequences
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0047795
Pubmed ID
Authors

Tuval Ben Yehezkel, Tamir Biezuner, Gregory Linshiz, Yair Mazor, Ehud Shapiro

Abstract

The extraordinary fidelity, sensory and regulatory capacity of natural intracellular machinery is generally confined to their endogenous environment. Nevertheless, synthetic bio-molecular components have been engineered to interface with the cellular transcription, splicing and translation machinery in vivo by embedding functional features such as promoters, introns and ribosome binding sites, respectively, into their design. Tapping and directing the power of intracellular molecular processing towards synthetic bio-molecular inputs is potentially a powerful approach, albeit limited by our ability to streamline the interface of synthetic components with the intracellular machinery in vivo. Here we show how a library of synthetic DNA devices, each bearing an input DNA sequence and a logical selection module, can be designed to direct its own probing and processing by interfacing with the bacterial DNA mismatch repair (MMR) system in vivo and selecting for the most abundant variant, regardless of its function. The device provides proof of concept for programmable, function-independent DNA selection in vivo and provides a unique example of a logical-functional interface of an engineered synthetic component with a complex endogenous cellular system. Further research into the design, construction and operation of synthetic devices in vivo may lead to other functional devices that interface with other complex cellular processes for both research and applied purposes.

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Geographical breakdown

Country Count As %
Australia 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 37%
Professor 5 26%
Researcher 4 21%
Lecturer 1 5%
Student > Master 1 5%
Other 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 68%
Biochemistry, Genetics and Molecular Biology 2 11%
Medicine and Dentistry 2 11%
Computer Science 1 5%
Unknown 1 5%