Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling.

Abstract:

:Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. 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-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (∼4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.

journal_name

ACS Synth Biol

journal_title

ACS synthetic biology

authors

Berepiki A,Kent R,Machado LFM,Dixon N

doi

10.1021/acssynbio.9b00448

subject

Has Abstract

pub_date

2020-03-20 00:00:00

pages

576-589

issue

3

issn

2161-5063

journal_volume

9

pub_type

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