期刊
BIOCHEMICAL SOCIETY TRANSACTIONS
卷 44, 期 -, 页码 731-737出版社
PORTLAND PRESS LTD
DOI: 10.1042/BST20160042
关键词
artificial neural networks; partial least squares modelling; promoter; synthetic biology; systems biology
The judicious choice of promoter to drive gene expression remains one of the most important considerations for synthetic biology applications. Constitutive promoter sequences isolated from nature are often used in laboratory settings or small-scale commercial production streams, but unconventional microbial chassis for new synthetic biology applications require well-characterized, robust and orthogonal promoters. This review provides an overview of the opportunities and challenges for synthetic promoter discovery and design, including molecular methodologies, such as saturation mutagenesis of flanking regions and mutagenesis by error-prone PCR, as well as the less familiar use of computational and statistical analyses for de novo promoter design.
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