Please use this identifier to cite or link to this item: https://doi.org/10.1038/srep04515
Title: Predictive combinatorial design of mRNA translation initiation regions for systematic optimization of gene expression levels
Authors: Seo, S.W
Yang, J.-S
Cho, H.-S 
Yang, J
Kim, S.C
Park, J.M
Kim, S
Jung, G.Y
Keywords: messenger RNA
algorithm
Escherichia coli
gene expression
gene library
genetics
metabolic engineering
procedures
RNA translation
Algorithms
Escherichia coli
Gene Expression
Gene Library
Metabolic Engineering
Peptide Chain Initiation, Translational
RNA, Messenger
Issue Date: 2014
Publisher: Nature Publishing Groups
Citation: Seo, S.W, Yang, J.-S, Cho, H.-S, Yang, J, Kim, S.C, Park, J.M, Kim, S, Jung, G.Y (2014). Predictive combinatorial design of mRNA translation initiation regions for systematic optimization of gene expression levels. Scientific Reports 4 : 4515. ScholarBank@NUS Repository. https://doi.org/10.1038/srep04515
Rights: Attribution 4.0 International
Abstract: Balancing the amounts of enzymes is one of the important factors to achieve optimum performance of a designed metabolic pathway. However, the random mutagenesis approach is impractical since it requires searching an unnecessarily large number of variants and often results in searching a narrow range of expression levels which are out of optimal level. Here, we developed a predictive combinatorial design method, called UTR Library Designer, which systematically searches a large combinatorial space of expression levels. It accomplishes this by designing synthetic translation initiation region of mRNAs in a predictive way based on a thermodynamic model and genetic algorithm. Using this approach, we successfully enhanced lysine and hydrogen production in Escherichia coli. Our method significantly reduced the number of variants to be explored for covering large combinatorial space and efficiently enhanced pathway efficiency, thereby facilitating future efforts in metabolic engineering and synthetic biology.
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/177767
ISSN: 20452322
DOI: 10.1038/srep04515
Rights: Attribution 4.0 International
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