» Articles » PMID: 16406119

Synthetic Promoter Libraries--tuning of Gene Expression

Overview
Specialty Biochemistry
Date 2006 Jan 13
PMID 16406119
Citations 67
Authors
Affiliations
Soon will be listed here.
Abstract

The study of gene function often requires changing the expression of a gene and evaluating the consequences. In principle, the expression of any given gene can be modulated in a quasi-continuum of discrete expression levels but the traditional approaches are usually limited to two extremes: gene knockout and strong overexpression. However, applications such as metabolic optimization and control analysis necessitate a continuous set of expression levels with only slight increments in strength to cover a specific window around the wild-type expression level of the studied gene; this requirement can be met by using promoter libraries. This approach generally consists of inserting a library of promoters in front of the gene to be studied, whereby the individual promoters might deviate either in their spacer sequences or bear slight deviations from the consensus sequence of a vegetative promoter. Here, we describe the two different methods for obtaining promoter libraries and compare their applicability.

Citing Articles

Exploring the Promoter Generation and Prediction of spp. Based on GAN and Multi-Model Fusion Methods.

Zhao C, Guan Y, Yan S, Li J Int J Mol Sci. 2024; 25(23).

PMID: 39684846 PMC: 11642183. DOI: 10.3390/ijms252313137.


Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules.

Shin J, Zielinski D, Palsson B Nucleic Acids Res. 2024; 52(18):11362-11377.

PMID: 39193902 PMC: 11472167. DOI: 10.1093/nar/gkae742.


CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Ren R, Yu H, Teng J, Mao S, Bian Z, Tao Y Brief Bioinform. 2024; 25(5).

PMID: 39120645 PMC: 11311715. DOI: 10.1093/bib/bbae398.


Natural promoters and promoter engineering strategies for metabolic regulation in Saccharomyces cerevisiae.

He S, Zhang Z, Lu W J Ind Microbiol Biotechnol. 2023; 50(1).

PMID: 36633543 PMC: 9936215. DOI: 10.1093/jimb/kuac029.


Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb.

Liebal U, Kobbing S, Netze L, Schweidtmann A, Mitsos A, Blank L Front Bioinform. 2022; 1:747428.

PMID: 36303772 PMC: 9581000. DOI: 10.3389/fbinf.2021.747428.