Song Kyeongseok, Ji Haekang, Lee Jiwon, Yoon Youngdae
Department of Environmental Health Science, Konkuk University, Seoul 05029, Republic of Korea.
Biosensors (Basel). 2025 Mar 31;15(4):221. doi: 10.3390/bios15040221.
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.
基于转录因子的生物传感器（TFB）是微生物生物传感器应用中的强大工具，可实现代谢途径的动态控制、细胞内代谢物的实时监测以及用于菌株工程的高通量筛选（HTS）。这些系统利用转录因子（TF）将代谢物浓度转化为可量化的输出，从而精确调节微生物细胞工厂中的代谢通量和生物合成效率。TFB的最新进展，包括提高的灵敏度、特异性和动态范围，拓宽了它们在合成生物学和工业生物技术中的应用。诸如Cello之类的计算工具进一步彻底改变了TFB设计，能够进行计算机优化并构建复杂的遗传电路，以整合多种信号并实现精确的基因调控。本文综述探讨了用于微生物生物传感器的TFB系统的创新、它们在代谢工程和适应性进化中的作用，以及它们未来与人工智能和先进筛选技术的整合，以克服合成生物学和工业生物生产中的关键挑战。
