title: Transcription factor-based biosensors in biotechnology: current state and future prospects.
abstract: Living organisms have evolved a plethora of sensing systems for the intra- and extracellular detection of small molecules, ions or physical parameters. Several recent studies have demonstrated that these principles can be exploited to devise synthetic regulatory circuits for metabolic engineering strategies. In this context, transcription factors (TFs) controlling microbial physiology at the level of transcription play a major role in biosensor design, since they can be implemented in synthetic circuits controlling gene expression in dependency of, for example, small molecule production. Here, we review recent progress on the utilization of TF-based biosensors in microbial biotechnology highlighting different areas of application. Recent advances in metabolic engineering reveal TF-based sensors to be versatile tools for strain and enzyme development using high-throughput (HT) screening strategies and adaptive laboratory evolution, the optimization of heterologous pathways via the implementation of dynamic control circuits and for the monitoring of single-cell productivity in live cell imaging studies. These examples underline the immense potential of TF-based biosensor circuits but also identify limitations and room for further optimization.
authors.affiliation: Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.
authors.forename: Regina
authors.initials: R
authors.lastname: Mahr
authors.affiliation: Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany. j.frunzke@fz-juelich.de.
authors.forename: Julia
authors.initials: J
authors.lastname: Frunzke
mesh_terms: D015374:Biosensing Techniques / Q000379:methods*; D001709:Biotechnology / Q000379:methods*; D053263:Gene Regulatory Networks; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism*
chemical_list: D014157:Transcription Factors
keywords: Biosensor; Evolution; Metabolic engineering; Screening; Single-cell analysis; Transcriptional regulator