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However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose\u2013response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (&amp;gt;500-fold), expanding the sensing range (\u223c4-orders of magnitude), increasing sensitivity (by &amp;gt;1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose\u2013response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.<\/jats:p>","DOI":"10.1021\/acssynbio.9b00448","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T19:02:28Z","timestamp":1580929348000},"page":"576-589","source":"Crossref","is-referenced-by-count":68,"title":["Development of High-Performance Whole Cell Biosensors\nAided by Statistical Modeling"],"prefix":"10.1021","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3881-0503","authenticated-orcid":true,"given":"Adokiye","family":"Berepiki","sequence":"first","affiliation":[{"name":"University of Manchester , , ,","place":["Manchester, U.K., M1 7DN"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ross","family":"Kent","sequence":"additional","affiliation":[{"name":"University of Manchester , , ,","place":["Manchester, U.K., M1 7DN"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leopoldo F. 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