Difference between revisions of "Characterization of minimum inducer separation time for a two-input integrase-based event detector"

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| url = http://www.cds.caltech.edu/~murray/preprints/hhm15-wqbio_s.pdf
| url = http://www.cds.caltech.edu/~murray/preprints/hhm15-wqbio_s.pdf
| abstract =  
| abstract =  
In this work, we present modeling and experimental characterization of the minimum time needed for flipping of a DNA substrate by a two-integrase event detector. The event detector logic di↵erentiates the temporal order of two chemical inducers. We find that bundling biological rate parameters (transcription, translation, DNA search- ing, DNA flipping) into only a few rate constants in a stochastic model is su�cient to accurately predict final DNA states. We show, through time course data in E.coli, that these modeling predictions are reproduced in vivo. We believe this model validation is critical for using integrase-based systems in larger circuits.
In this work, we present modeling and experimental characterization of the minimum time needed for flipping of a DNA substrate by a two-integrase event detector. The event detector logic di↵erentiates the temporal order of two chemical inducers. We find that bundling biological rate parameters (transcription, translation, DNA search- ing, DNA flipping) into only a few rate constants in a stochastic model is sufficient to accurately predict final DNA states. We show, through time course data in E.coli, that these modeling predictions are reproduced in vivo. We believe this model validation is critical for using integrase-based systems in larger circuits.
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| filetype = PDF
| filetype = PDF

Latest revision as of 02:11, 18 May 2016


Victoria Hsiao, Yutaka Hori, and Richard M Murray
Presented, 2015 Winter q-bio Conference (5 Nov 2014)

In this work, we present modeling and experimental characterization of the minimum time needed for flipping of a DNA substrate by a two-integrase event detector. The event detector logic diâµerentiates the temporal order of two chemical inducers. We find that bundling biological rate parameters (transcription, translation, DNA search- ing, DNA flipping) into only a few rate constants in a stochastic model is sufficient to accurately predict final DNA states. We show, through time course data in E.coli, that these modeling predictions are reproduced in vivo. We believe this model validation is critical for using integrase-based systems in larger circuits.