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中国精品科技期刊2020
张超,张玥,刘飞,等. 乳酸乳球菌合成生物学工具研究进展[J]. 食品工业科技,2025,46(24):1−11. doi: 10.13386/j.issn1002-0306.2025020046.
引用本文: 张超,张玥,刘飞,等. 乳酸乳球菌合成生物学工具研究进展[J]. 食品工业科技,2025,46(24):1−11. doi: 10.13386/j.issn1002-0306.2025020046.
ZHANG Chao, ZHANG Yue, LIU Fei, et al. Research Progress on Synthetic Biotechnology T ools of Lactococcus lactis[J]. Science and Technology of Food Industry, 2025, 46(24): 1−11. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2025020046.
Citation: ZHANG Chao, ZHANG Yue, LIU Fei, et al. Research Progress on Synthetic Biotechnology T ools of Lactococcus lactis[J]. Science and Technology of Food Industry, 2025, 46(24): 1−11. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2025020046.

乳酸乳球菌合成生物学工具研究进展

Research Progress on Synthetic Biotechnology T ools of Lactococcus lactis

  • 摘要: 乳酸乳球菌(Lactococcus lactis)作为一种典型的益生菌,因其遗传背景清晰、代谢路径简洁且符合食品级安全标准等特点,近年来在合成生物学领域展现出显著的工业应用潜力。本文系统综述了乳酸乳球菌合成生物学工具的最新研究进展,重点讨论了基因组规模代谢网络模型(genome scale of metabolic network model,GSMM)、基因表达系统以及基因组编辑工具的开发与应用。通过精确调控乳酸乳球菌的代谢途径,研究者不仅推动乳酸乳球菌从传统发酵菌株向智能生物工厂转型,还为其在功能性食品开发、靶向药物递送及绿色化学品合成等领域的应用奠定了基础。未来需进一步整合机器学习驱动的多组学建模与动态网络调控,以实现更高精度的代谢设计与产业化应用。

     

    Abstract: Lactococcus lactis, a well-characterized probiotic bacterium with a simple metabolic network and "Generally Recognized as Safe" status, has emerged as a promising chassis in synthetic biology with broad industrial applications. This review provides a comprehensive overview of recent advances in synthetic biology tools developed for L. lactis, focusing on the construction and application of genome-scale metabolic models, gene expression systems, and genome-editing technologies. Through the precise modulation of metabolic pathways, researchers have accelerated the transition of L. lactis from a traditional fermentation strain to an intelligent microbial cell factory, facilitating its use in the development of functional foods, targeted drug delivery systems, and the sustainable production of bio-based chemicals. Future efforts should aim to integrate machine learning-driven multi-omics modeling with dynamic regulatory networks to achieve more precise metabolic design and scalable industrial deployment.

     

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