Predictive combinatorial design of mRNA translation initiation regions for systematic optimization of gene expression levels
2014 · Scientific Reports
저자
서상우 (Sang Woo Seo), Jae‐Seong Yang, Han‐Saem Cho, Jina Yang, Seong‐Cheol Kim, Jong Moon Park, Sanguk Kim, Gyoo Yeol Jung
연구주제
Microbial Metabolic Engineering and Bioproduction · RNA and protein synthesis mechanisms
NTIS 과학기술표준분류
가장 가까운 분류(생명과학 > 유전학·유전체학)가 화학·화공 범위 밖이다. 배정 근거: 지원 과제·키워드 근거가 없어 AI가 논문 제목·초록을 읽고 고른 분류.
AI 연구 키워드·방법
gene expression optimization metabolic pathway optimization synthetic 5' untranslated region translation initiation region
genetic algorithm thermodynamic modeling
키워드 (OpenAlex)
gene expression optimization combinatorial designs thermodynamic modeling genetic algorithm synthetic promoters metabolic pathway engineering lysine production hydrogen production
초록
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 efficie…
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