Microbial Transcription Factor-Based Biosensors: Innovations from Design to Applications in Synthetic Biology

Abstract

Transcription factor-based biosensors (TFBs) are powerful tools in microbial biosensor applications, enabling dynamic control of metabolic pathways, real-time monitoring of intracellular metabolites, and high-throughput screening (HTS) for strain engineering. These systems use transcription factors (TFs) to convert metabolite concentrations into quantifiable outputs, enabling precise regulation of metabolic fluxes and biosynthetic efficiency in microbial cell factories. Recent advancements in TFB, including improved sensitivity, specificity, and dynamic range, have broadened their applications in synthetic biology and industrial biotechnology. Computational tools such as Cello have further revolutionized TFB design, enabling in silico optimization and construction of complex genetic circuits for integrating multiple signals and achieving precise gene regulation. This review explores innovations in TFB systems for microbial biosensors, their role in metabolic engineering and adaptive evolution, and their future integration with artificial intelligence and advanced screening technologies to overcome critical challenges in synthetic biology and industrial bioproduction.

Keywords: dynamic metabolic control; genetic engineering; high-throughput screening (HTS); strain evolution; transcription factors (TFs)-based biosensors.

Figure 1

Working mechanisms of TFB systems and the effects of biocomponents. (a) Working mechanisms of activator-based and repressor-based TFB systems. The interaction between TFs and targets affects the expression of reporter genes, and it was translated as output signals. (b) Effects of genetic engineering of biocomponents, including promoter, RBS, TFBS, operator, and TFs, on the performances of TFB systems.

Figure 2

Genetic engineering strategies to modulate the performance of TFB systems. (a) The strategies to modulate the characteristics of TFs by genetic engineering. (b) Genetic engineering on promoter, RBS, and TFBS. (c) Computer-assisted engineering on biocomponents.