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

Research output: Contribution to journalArticlepeer-review

  • Authors:
  • Adokiye Berepiki
  • Ross Kent
  • Leopoldo F.m. Machado
  • Neil Dixon

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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)576-589
Number of pages14
JournalACS Synthetic Biology
Volume9
Issue number3
Early online date5 Feb 2020
DOIs
Publication statusPublished - 20 Mar 2020

Related information