PBD, Plackett–Burman design

  • 文章类型: Journal Article
    2015年诺贝尔生理学或医学奖授予阿维菌素和青蒿素,分别。阿维链霉菌产生的阿维菌素是极好的驱虫药和潜在的抗生素。因为野生型菌株只产生低水平的阿维菌素,许多研究工作都集中在改善阿维菌素的生产,以满足对此类化合物不断增长的需求。本文综述了合成生物学在提高阿维菌素产量方面的广泛应用策略和未来应用前景。借助阿维菌素的基因组测序和对阿维菌素生物合成/调节途径的理解,合成和系统生物技术方法已应用于精密工程。我们专注于生物底盘的设计和合成,零件,设备,以及来自不同微生物的模块来重建和优化它们的动态过程,以及通过4Ms策略(Mine,型号,操纵,和测量)。
    The 2015 Nobel Prize in Physiology or Medicine has been awarded to avermectins and artemisinin, respectively. Avermectins produced by Streptomyces avermitilis are excellent anthelmintic and potential antibiotic agents. Because wild-type strains only produce low levels of avermectins, much research effort has focused on improvements in avermectin production to meet the ever increasing demand for such compounds. This review describes the strategies that have been widely employed and the future prospects of synthetic biology applications in avermectin yield improvement. With the help of genome sequencing of S. avermitilis and an understanding of the avermectin biosynthetic/regulatory pathways, synthetic and systems biotechnology approaches have been applied for precision engineering. We focus on the design and synthesis of biological chassis, parts, devices, and modules from diverse microbes to reconstruct and optimize their dynamic processes, as well as predict favorable effective overproduction of avermectins by a 4Ms strategy (Mine, Model, Manipulation, and Measurement).
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  • 文章类型: Journal Article
    甲基杆菌属。通过系统优化发酵培养基,采用zju323提高吡咯并喹啉醌(PQQ)的生物合成。Plackett-Burman设计用于筛选PQQ生产的关键介质组件。CoCl2·6H2O,对-氨基苯甲酸,和MgSO4·7H2O能够最显著地提高PQQ的产量。使用五水平三因素中心复合设计来研究这些变量的直接和交互影响。响应面法(RSM)和人工神经网络-遗传算法(ANN-GA)均用于预测PQQ产量并优化培养基组成。结果表明,ANN-GA优化的培养基在最大程度地提高PQQ产量方面优于RSM,ANN-GA优化的培养基中的实验PQQ浓度比未优化的培养基提高了44.3%。进一步的研究表明,这种ANN-GA优化的培养基也可以通过分批补料模式有效地提高PQQ的产量。达到232.0mg/L的最高PQQ积累,相对于原始培养基增加了约47.6%。本工作提供了一种优化的培养基,并开发了一种补料分批策略,该策略可能可能适用于工业PQQ生产。
    Methylobacillus sp. zju323 was adopted to improve the biosynthesis of pyrroloquinoline quinone (PQQ) by systematic optimization of the fermentation medium. The Plackett-Burman design was implemented to screen for the key medium components for the PQQ production. CoCl2 · 6H2O, ρ-amino benzoic acid, and MgSO4 · 7H2O were found capable of enhancing the PQQ production most significantly. A five-level three-factor central composite design was used to investigate the direct and interactive effects of these variables. Both response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) were used to predict the PQQ production and to optimize the medium composition. The results showed that the medium optimized by ANN-GA was better than that by RSM in maximizing PQQ production and the experimental PQQ concentration in the ANN-GA-optimized medium was improved by 44.3% compared with that in the unoptimized medium. Further study showed that this ANN-GA-optimized medium was also effective in improving PQQ production by fed-batch mode, reaching the highest PQQ accumulation of 232.0 mg/L, which was about 47.6% increase relative to that in the original medium. The present work provided an optimized medium and developed a fed-batch strategy which might be potentially applicable in industrial PQQ production.
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