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Robustness of DNA Repair through Collective Rate Control

Overview of attention for article published in PLoS Computational Biology, January 2014
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Title
Robustness of DNA Repair through Collective Rate Control
Published in
PLoS Computational Biology, January 2014
DOI 10.1371/journal.pcbi.1003438
Pubmed ID
Authors

Paul Verbruggen, Tim Heinemann, Erik Manders, Gesa von Bornstaedt, Roel van Driel, Thomas Höfer

Abstract

DNA repair and other chromatin-associated processes are carried out by enzymatic macromolecular complexes that assemble at specific sites on the chromatin fiber. How the rate of these molecular machineries is regulated by their constituent parts is poorly understood. Here we quantify nucleotide-excision DNA repair in mammalian cells and find that, despite the pathways' molecular complexity, repair effectively obeys slow first-order kinetics. Theoretical analysis and data-based modeling indicate that these kinetics are not due to a singular rate-limiting step. Rather, first-order kinetics emerge from the interplay of rapidly and reversibly assembling repair proteins, stochastically distributing DNA lesion repair over a broad time period. Based on this mechanism, the model predicts that the repair proteins collectively control the repair rate. Exploiting natural cell-to-cell variability, we corroborate this prediction for the lesion-recognition factor XPC and the downstream factor XPA. Our findings provide a rationale for the emergence of slow time scales in chromatin-associated processes from fast molecular steps and suggest that collective rate control might be a widespread mode of robust regulation in DNA repair and transcription.

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Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Germany 1 3%
Italy 1 3%
Unknown 36 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 26%
Researcher 9 23%
Student > Bachelor 4 10%
Student > Master 4 10%
Professor > Associate Professor 3 8%
Other 6 15%
Unknown 3 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 28%
Agricultural and Biological Sciences 11 28%
Physics and Astronomy 5 13%
Computer Science 3 8%
Mathematics 2 5%
Other 2 5%
Unknown 5 13%