面向工业生物技术的系统生物学
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摘要
工业生物技术是以微生物细胞工厂利用可再生的生物原料来生产能源、材料与化学品等的生物技术,在解决资源、能源与环境等问题方面起着越来越重要的作用。系统生物学是全面解析微生物细胞工厂及其发酵过程从"黑箱"到"白箱"的重要研究方法。系统生物学借助基因组、转录组、蛋白质组、代谢组以及代谢流组等多组学数据,可解析微生物细胞工厂在RNA、蛋白与代谢物等不同水平上的变化规律与调控机制。目前,系统生物学在微生物细胞工厂的设计创建与发酵工艺优化中起着越来越重要的指导作用,许多成功应用实例不断涌现,推动着工业生物技术的快速发展。文中重点综述基因组、转录组、蛋白质组、代谢组与代谢流组以及基因组规模的网络模型等各组学技术的最新发展及其在工业生物技术尤其是菌株改造与发酵优化中的应用,并就工业生物技术中系统生物学的未来发展方向进行展望。
In industrial biotechnology, microbial cell factories utilize renewable resources to produce energy, materials and chemicals. Industrial biotechnology plays an increasingly important role in solving the resource, energy and environmental problems. Systems biology has shed new light on industrial biotechnology, deepening our understanding of industrial microbial cell factories and their bioprocess from "Black-box" to "White-box". Systems-wide profiling of genome, transcriptome, proteome, metabolome, and fluxome has proven valuable to better unveil network operation and regulation on the genome scale. System biology has been successfully applied to create microbial cell factories for numerous products and derive attractive industrial processes, which has constantly expedited the development of industrial biotechnology. This review focused on the recent advance and applications of omics and trans-omics in industrial biotechnology, including genomics, transcriptomics, proteomics, metabolomics, fluxomics and genome scale modeling, and so on. Furthermore, this review also discussed the potential and promise of systems biology in industrial biotechnology.
In industrial biotechnology, microbial cell factories utilize renewable resources to produce energy, materials and chemicals. Industrial biotechnology plays an increasingly important role in solving the resource, energy and environmental problems. Systems biology has shed new light on industrial biotechnology, deepening our understanding of industrial microbial cell factories and their bioprocess from "Black-box" to "White-box". Systems-wide profiling of genome, transcriptome, proteome, metabolome, and fluxome has proven valuable to better unveil network operation and regulation on the genome scale. System biology has been successfully applied to create microbial cell factories for numerous products and derive attractive industrial processes, which has constantly expedited the development of industrial biotechnology. This review focused on the recent advance and applications of omics and trans-omics in industrial biotechnology, including genomics, transcriptomics, proteomics, metabolomics, fluxomics and genome scale modeling, and so on. Furthermore, this review also discussed the potential and promise of systems biology in industrial biotechnology.
引文
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[16] Gan CS, Chong PK, Pham TK, et al. Technical,experimental, and biological variations in isobaric tags for relative and absolute quantitation(iTRAQ).J Proteome Res, 2007, 6(2):821–827.
[17] Vizcaíno JA, C?téRG, Csordas A, et al. The PRoteomics IDEntifications(PRIDE)database and associated tools:status in 2013. Nucleic Acids Res,2013, 41(D1):D1063–D1069.
[18] Craig R, Cortens JP, Beavis RC. Open source system for analyzing, validating, and storing protein identification data. J Proteome Res, 2004, 3(6):1234–1242.
[19] McCord J, Sun Z, Deutsch EW, et al. The PeptideAtlas of the domestic laying hen. J Proteome Res, 2017, 16(3):1352–1363.
[20] The UniProt Consortium. UniProt:a worldwide hub of protein knowledge. Nucleic Acids Res, 2019,47(D1):D506–D515.
[21] Smith AC, Robinson AJ. MitoMiner v3.1, an update on the mitochondrial proteomics database. Nucleic Acids Res, 2016, 44(D1):D1258–D1261.
[22] Nanjappa V, Thomas JK, Marimuthu A, et al.Plasma proteome database as a resource for proteomics research:2014 update. Nucleic Acids Res, 2014, 42(D1):D959–D965.
[23] Urbanczyk-Wochniak E, Luedemann A, Kopka J, et al. Parallel analysis of transcript and metabolic profiles:a new approach in systems biology. EMBO Rep, 2003, 4(10):989–993.
[24] Zhang QQ, Zheng XM, Wang Y, et al.Comprehensive optimization of the metabolomic methodology for metabolite profiling of Corynebacterium glutamicum. Appl Microbiol Biotechnol, 2018, 102(16):7113–7121.
[25] Zheng XM, Yu JD, Cairns TC, et al. Comprehensive improvement of sample preparation methodologies facilitates dynamic metabolomics of Aspergillus niger. Biotechnol J, 2019, 14(3):1800315.
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[27] Kim S, Lee DY, Wohlgemuth G, et al. Evaluation and optimization of metabolome sample preparation methods for Saccharomyces cerevisiae. Anal Chem,2013, 85(4):2169–2176.
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