Liu Yang, Liu Ye, Wang Meng
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
Front Microbiol. 2017 Oct 17;8:2012. doi: 10.3389/fmicb.2017.02012. eCollection 2017.
The development of synthetic biology and metabolic engineering has painted a great future for the bio-based economy, including fuels, chemicals, and drugs produced from renewable feedstocks. With the rapid advance of genome-scale modeling, pathway assembling and genome engineering/editing, our ability to design and generate microbial cell factories with various phenotype becomes almost limitless. However, our lack of ability to measure and exert precise control over metabolite concentration related phenotypes becomes a bottleneck in metabolic engineering. Genetically encoded small molecule biosensors, which provide the means to couple metabolite concentration to measurable or actionable outputs, are highly promising solutions to the bottleneck. Here we review recent advances in the design, optimization and application of small molecule biosensor in metabolic engineering, with particular focus on optimization strategies for transcription factor (TF) based biosensors.
合成生物学和代谢工程的发展为生物基经济描绘了美好的未来,包括利用可再生原料生产燃料、化学品和药物。随着基因组规模建模、途径组装以及基因组工程/编辑技术的迅速发展,我们设计和构建具有各种表型的微生物细胞工厂的能力几乎变得无限。然而,我们在测量和精确控制与代谢物浓度相关的表型方面能力的不足,成为了代谢工程中的一个瓶颈。基因编码的小分子生物传感器能够将代谢物浓度与可测量或可操作的输出联系起来,是解决这一瓶颈的极具前景的方案。在此,我们综述了小分子生物传感器在代谢工程中的设计、优化及应用方面的最新进展,尤其关注基于转录因子(TF)的生物传感器的优化策略。