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使用遗传算法优化昆虫细胞补料分批培养的培养基

Optimization of a feed medium for fed-batch culture of insect cells using a genetic algorithm.

作者信息

Marteijn R C L, Jurrius O, Dhont J, de Gooijer C D, Tramper J, Martens D E

机构信息

Wageningen University, Department of Agrotechnology and Food Sciences, Food and Bioprocess Engineering Group, P.O. Box 8129, 6700 EV, Wageningen, The Netherlands.

出版信息

Biotechnol Bioeng. 2003 Feb 5;81(3):269-78. doi: 10.1002/bit.10465.

Abstract

Insect cells have been cultured for over 30 years, but their application is still hampered by low cell densities in batch fermentations and expensive culture media. With respect to the culture method, the fed-batch culture mode is often found to give the best yields. However, optimization of the feed composition is usually a laborious task. In this report, the successful use of genetic algorithms (GAs) to optimize the growth of insect cells is described. A feed was developed from 11 different medium components, each used at a wide range of concentrations. The feed was optimized within four sets of 20 experiments. The optimized feed was tested in bioreactors and the addition scheme was further improved. The viable-cell density of HzAm1 (Helicoverpa zea) insect cells improved 550% to 19.5 x 10(6) cells/mL compared to a control fermentation in an optimized commercial medium. No accumulation of waste products was found, and none of the amino acids was depleted. Glucose was depleted, which suggests that even further improvement is possible. We show that GAs are a successful method to optimize a complex fermentation in a relatively short time frame and without the need of detailed information concerning the cellular physiology or metabolism.

摘要

昆虫细胞已培养了30多年,但它们的应用仍受到分批发酵中细胞密度低和培养基昂贵的阻碍。就培养方法而言,补料分批培养模式通常能获得最佳产量。然而,优化补料成分通常是一项艰巨的任务。在本报告中,描述了成功使用遗传算法(GA)来优化昆虫细胞生长的情况。一种补料由11种不同的培养基成分制成,每种成分都在很宽的浓度范围内使用。在四组20次实验中对补料进行了优化。在生物反应器中对优化后的补料进行了测试,并进一步改进了添加方案。与在优化的商业培养基中进行的对照发酵相比HzAm1(玉米螟)昆虫细胞的活细胞密度提高了550%,达到19.5×10⁶个细胞/毫升。未发现废物积累,且没有氨基酸被耗尽。葡萄糖被耗尽,这表明甚至可能有进一步的改进。我们表明,遗传算法是一种在相对较短的时间内优化复杂发酵的成功方法,并且无需有关细胞生理学或代谢的详细信息。

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