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KMID : 1007520140230010201
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Food Science and Biotechnology
2014 Volume.23 No. 1 p.201 ~ p.207
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Empirical Predictive Modelling of Poly-ε-lysine Biosynthesis in Resting Cells of Streptomyces noursei
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Sandip Bankar
Vivek Dhumal
Devshri Bhotmange
Sunil Bhagwat
Rekha Singhal
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Abstract
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Poly-ε-L-lysine (ε-PL) biosynthesis wasinvestigated using the resting cell culture technique andnutritional parameters were optimized with responsesurface methodology (RSM) and an artificial neuralnetwork (ANN). ε-PL production in resting cell cultures ofStreptomyces noursei NRRL 5126 was compared usingRSM and ANN optimization techniques. The predicted ε-PL yield of 924.65 mg/L using ANN simulation was inbetter agreement with validation experimental results of918.35±7.56 mg/L than RSM simulation results of 966.24mg/L. The optimized medium consisted of 3% glucose,1% ammonium sulphate, and 5 mM citric acid in both ashake flask and a 5 L bioreactor. The shake flask ε-PLproduction as 1.0 g/L and bioreactor production as 2.36 g/L was observed. The ANN predictive model was betterthan the RSM predictive model during nonlinear behaviorof the system.
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KEYWORD
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artificial neural network, poly-ε-L-lysine, response surface methodology, resting cell culture, Streptomyces noursei
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