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Sci Rep. 2014 Mar 31;4:4515. doi: 10.1038/srep04515.

Predictive combinatorial design of mRNA translation initiation regions for systematic optimization of gene expression levels.

Author information

1
1] Department of Chemical Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk, Korea, 790-784 [2].
2
1] School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk, Korea, 790-784 [2].
3
Department of Chemical Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk, Korea, 790-784.
4
School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk, Korea, 790-784.
5
1] Division of Molecular and Life Science, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk, Korea, 790-784 [2] Division of IT Convergence Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk, Korea, 790-784.
6
1] Department of Chemical Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk, Korea, 790-784 [2] School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk, Korea, 790-784.

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.

PMID:
24682040
PMCID:
PMC3970122
DOI:
10.1038/srep04515
[Indexed for MEDLINE]
Free PMC Article

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