A biostatistical approach for augmenting rare bianthraquinone antibiotic production by Streptomyces sp. RA-WS2 using Taguchi design

Abstract

Consistent production of bioactives from microbial sources remains a big challenge for fermentation based bio-processes. Setomimycin, a rare 9,9'-bianthrylanthracene antibiotic reported to be active against Gram positive bacteria i.e. Staphyloccocus aureus, Bacillus subtilis, Bacillus cereus, and Mycobacterium smegmatis, including mycobacteria is one of the least exploited antibiotic. Present work aims to enhance and maximize setomimycin production using One Factor at a Time (OFAT) approach, followed by Taguchi L9 orthogonal array (OA) design in 30L fermenter. Four most influential parameters, namely carbon source, nitrogen source, air and agitation were selected for optimization studies. The optimized production medium supplemented with 150 g/L glycerol and 7.5 g/L soyabean meal with an agitation rate of 100 RPM and air flow rate of 20 LPM (Liters Per Minute) resulted in 675 mg/L setomimycin production within 96–108 h of fermentation as compared to the initial production i.e. 40 mg/L. Thus, an overall enhancement of 16.8 folds was achieved in setomimycin production after optimization in 30L fermenter. Graphical Abstract

Modeling and Optimization of the Culture Medium for Efficient 4′-N-Demethyl-Vicenistatin Production by Streptomyces parvus Using Response Surface Methodology and Artificial-Neural-Network-Genetic-Algorithm

“…In this study, the ANN-GA model demonstrated a superior performance over the RSM model in optimizing the production of 4 ′ -N-demethyl-vicenistatin by S. parvus SCSIO Mla-L010/∆vicG, both in terms of example verification and overall model performance. It has been widely acknowledged that researchers in the field of microbial fermentation optimization commonly employ conventional statistical methods, including the OFAT approach [74], orthogonal experiments [75], PBD [76][77][78], and RSM [14,79,80], for optimization purposes. For instance, the RSM, whether it is the Box-Behnken design (BBD) or CCD, is generally applicable when there are no more than four experimental factors.…”

Optimization of a tri-drug treatment against lung cancer using orthogonal design in preclinical studies

“…Each of the concentrations represented 1 level ( Table 1 ). Thus, a 3-factor, 3-level L9(3 3 ) OED was selected and applied to the experiments ( Table 2 ) ( Manhas, Kumar & Chaubey, 2022 ).…”