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Empirical predictive modelling of poly-ɛ-lysine biosynthesis in resting cells of Streptomyces noursei

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Abstract

Poly-ɛ-l-lysine (ɛ-PL) biosynthesis was investigated using the resting cell culture technique and nutritional parameters were optimized with response surface methodology (RSM) and an artificial neural network (ANN). ɛ-PL production in resting cell cultures of Streptomyces noursei NRRL 5126 was compared using RSM and ANN optimization techniques. The predicted ɛ-PL yield of 924.65 mg/L using ANN simulation was in better agreement with validation experimental results of 918.35±7.56 mg/L than RSM simulation results of 966.24 mg/L. The optimized medium consisted of 3% glucose, 1% ammonium sulphate, and 5 mM citric acid in both a shake flask and a 5 L bioreactor. The shake flask ɛ-PL production as 1.0 g/L and bioreactor production as 2.36 g/ L was observed. The ANN predictive model was better than the RSM predictive model during nonlinear behavior of the system.

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Authors and Affiliations

  1. Food Engineering and Technology Department, Institute of Chemical Technology, Matunga (E), Mumbai, 400 019, India

    Sandip Bankar, Devshri Bhotmange & Rekha Singhal

  2. Chemical Engineering Department, Institute of Chemical Technology, Matunga (E), Mumbai, 400 019, India

    Vivek Dhumal & Sunil Bhagwat

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  1. Sandip Bankar
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  2. Vivek Dhumal
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  3. Devshri Bhotmange
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  4. Sunil Bhagwat
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  5. Rekha Singhal
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Correspondence to Sandip Bankar.

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Bankar, S., Dhumal, V., Bhotmange, D. et al. Empirical predictive modelling of poly-ɛ-lysine biosynthesis in resting cells of Streptomyces noursei . Food Sci Biotechnol 23, 201–207 (2014). https://doi.org/10.1007/s10068-014-0027-2

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  • DOI: https://doi.org/10.1007/s10068-014-0027-2

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