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基于人工神经网络和遗传算法的普鲁兰酶重组大肠杆菌高密度发酵工艺优化

Translated title of the contribution: Artificial Neural Network-Genetic Algorithm-Based Optimization of High Cell Density Cultivation of Recombinant Escherichia coli for Producing Pullulanase
  • Lei Chi
  • , Jingyu Wang
  • , Junchao Hou
  • , Jiajia Wei
  • , Tao Wei
  • , Xiaolong Hu
  • , Peixin He*
  • *Corresponding author for this work
  • Zhengzhou University of Light Industry

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, the high cell density cultivation of recombinant Escherichia coli BL 21 for the production of a novel thermostable pullulanase was optimized using artificial neural network and genetic algorithm. The effects of culture temperature, medium pH, and carbon-to-nitrogen (C/N) molar ratio were tested in a 5 L bioreactor. The results suggested that the optimal culture conditions before the induction phase were as follows: temperature 34.4 ℃, pH 6.87 and C/N ratio 6.1, and the optimal culture conditions after induction were 32.5 ℃, pH 6.69 and 5.3 C/N ratio. The maximum biomass, protein concentration and pullulanase activity obtained under these conditions were 56.5 g/L, 3.21 g/L and 268.3 U/mL, respectively.

Translated title of the contributionArtificial Neural Network-Genetic Algorithm-Based Optimization of High Cell Density Cultivation of Recombinant Escherichia coli for Producing Pullulanase
Original languageChinese (Traditional)
Pages (from-to)73-78
Number of pages6
JournalShipin Kexue/Food Science
Volume42
Issue number10
DOIs
StatePublished - 25 May 2021
Externally publishedYes

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