Artificial intelligence-based optimization for extracellular L-glutaminase free L-asparaginase production by Streptomyces violaceoruber under solid state fermentation conditions

Résumé

Abstract The bacterial L-asparaginase is a highly effective chemotherapeutic drug and a cornerstone of treatment protocols used for treatment the acute lymphoblastic leukemia in pediatric oncology. A potential actinomycete isolate, Streptomyces sp. strain NEAE-99, produces glutaminase-free L-asparaginase was isolated from a soil sample. This potential strain was identified as S. violaceoruber strain NEAE-99. The central composite design (CCD) approach was utilized for finding the optimal values for four variables including the mixture of soybean and wheat bran in a 1:1 ratio (w/w), the concentrations of dextrose, L-asparagine, and potassium nitrate under solid state fermentation conditions. Through the use of an artificial neural network (ANN), the production of L-asparaginase by S. violaceoruber has been investigated, validated, and predicted in comparison to CCD. It was found that the optimal predicted conditions for maximum L-asparaginase production (216.19 U/gds) were 8.46 g/250 mL Erlenmeyer flask of soybean and wheat bran mixture in a 1:1 ratio (w/w), 2.2 g/L of dextrose, 18.97 g/L of L-asparagine, and 1.34 g/L of KNO3. The experimental results (207.55 U/gds) closely approximated the theoretical values (216.19 U/gds), as evidenced by the validation. This suggests that the ANN exhibited a high degree of precision and predictive capability.

Citer ce document

Accès au document

Texte intégral en lecture en ligne, réservé aux abonnés SPHAERO et aux membres de l'institution. Se connecter

Auteur(s)

Statistiques

Consultations : 1

Téléchargements : 0