Real encoded genetic algorithm and response surface methodology to optimize production of an indolizidine alkaloid, swainsonine, from Metarhizium anisopliae
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
- MeSH
- alkaloidy izolace a purifikace metabolismus MeSH
- biotechnologie metody MeSH
- fermentace MeSH
- kultivační média chemie MeSH
- Metarhizium genetika metabolismus MeSH
- neuronové sítě MeSH
- počet mikrobiálních kolonií MeSH
- swainsonin izolace a purifikace metabolismus MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- alkaloidy MeSH
- kultivační média MeSH
- swainsonin MeSH
Response surface methodology (RSM) and artificial neural network-real encoded genetic algorithm (ANN-REGA) were employed to develop a process for fermentative swainsonine production from Metarhizium anisopliae (ARSEF 1724). The effect of finally screened process variables viz. inoculum size, oatmeal extract, glucose, and CaCl2 were investigated through central composite design and were further utilized for training sets in ANN with training and test R values of 0.99 and 0.94, respectively. ANN-REGA was finally employed to simulate the predictive swainsonine production with best evolved media composition. ANN-REGA predicted a more precise fermentation model with 103 % (shake flask) increase in alkaloid production compared to 75.62 % (shake flask) obtained with RSM model upon validation.
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