000229765 001__ 229765
000229765 005__ 20181203024749.0
000229765 0247_ $$2doi$$a10.1021/acs.synbio.6b00364
000229765 022__ $$a2161-5063
000229765 02470 $$2ISI$$a000402026600012
000229765 037__ $$aARTICLE
000229765 245__ $$aMechanistic Modeling of Genetic Circuits for ArsR Arsenic Regulation
000229765 260__ $$aWashington$$bAmer Chemical Soc$$c2017
000229765 269__ $$a2017
000229765 300__ $$a13
000229765 336__ $$aJournal Articles
000229765 520__ $$aBioreporters are living cells that generate an easily measurable signal in the presence of a chemical compound. They acquire their functionality from synthetic gene circuits, the configuration of which defines the response signal and signal-to-noise ratio. Bioreporters based on the Escherichia coli ArsR system have raised significant interest for quantifying arsenic pollution, but they need to be carefully optimized to accurately work in the required low concentration range (1-10 mu g arsenite L-1). To better understand the general functioning of ArsR-based genetic circuits, we developed a comprehensive mechanistic model that was empirically tested and validated in E. coli carrying different circuit configurations. The model accounts for the different elements in the circuits (proteins, DNA, chemical species), and their detailed affinities and interactions, and predicts the (fluorescent) output from the bioreporter cell as a function of arsenite concentration. The model was parametrized using existing ArsR biochemical data, and then complemented by parameter estimations from the accompanying experimental data using a scatter search algorithm. Model predictions and experimental data were largely coherent for feedback and uncoupled circuit configurations, different ArsR alleles, promoter strengths, and presence or absence of arsenic efflux in the bioreporters. Interestingly, the model predicted a particular useful circuit variant having steeper response at low arsenite concentrations, which was experimentally confirmed and may be useful as arsenic bioreporter in the field. From the extensive validation we expect the mechanistic model to further be a useful framework for detailed modeling of other synthetic circuits.
000229765 6531_ $$abacterial bioreporters
000229765 6531_ $$aEscherichia coli
000229765 6531_ $$aordinary differential equations
000229765 6531_ $$aDNA binding affinity
000229765 700__ $$0247035$$aBerset, Yves$$g231662$$uUniv Lausanne, Dept Fundamental Microbiol, Batiment Biophore,Quartier UNIL Sorge, CH-1015 Lausanne, Switzerland
000229765 700__ $$aMerulla, Davide$$uUniv Lausanne, Dept Fundamental Microbiol, Batiment Biophore,Quartier UNIL Sorge, CH-1015 Lausanne, Switzerland
000229765 700__ $$aJoublin, Aurelie$$uUniv Lausanne, Dept Fundamental Microbiol, Batiment Biophore,Quartier UNIL Sorge, CH-1015 Lausanne, Switzerland
000229765 700__ $$0240657$$aHatzimanikatis, Vassily$$g174688
000229765 700__ $$aVan Der Meer, Jan R.
000229765 773__ $$j6$$k5$$q862-874$$tAcs Synthetic Biology
000229765 909C0 $$0252131$$pLCSB$$xU11422
000229765 909CO $$ooai:infoscience.tind.io:229765$$pSB$$particle
000229765 917Z8 $$x174688
000229765 937__ $$aEPFL-ARTICLE-229765
000229765 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000229765 980__ $$aARTICLE