Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations include an inadequate number of well-characterized promoters, poor dynamic range, and insufficient orthogonality to endogenous regulations. Therefore, it is necessary to perform promoter engineering to create synthetic promoters with better properties. Here, we review recent advances related to promoter architecture, promoter engineering and synthetic promoter applications in S. cerevisiae. We also provide a perspective of future directions in this field with an emphasis on the recent advances of machine learning based promoter designs.
Tang Hongting, Wu Yanling, Deng Jiliang, Chen Nanzhu, Zheng Zhaohui, Wei Yongjun, Luo Xiaozhou, Keasling Jay D
Saccharomyces cerevisiae, machine learning, promoter architecture, promoter engineering, synthetic biology, synthetic promoter