Response surface methodology-based central composite design on five variables incubation time, pH, temperature, sucrose concentration, and soya peptone concentration was employed for optimization of the production of bioactive compounds by Nocardiopsis litoralis strain VSM 8. The main quadratic effects and interactions of the five variables on the production of bioactive metabolites were investigated. A second-order polynomial model produced a satisfied fit for experimental data with regard to the production of the bioactive metabolites. Regression analysis showed that high R 2 values of all the five responses are significant and adjusted R 2 values showed good agreement with the experimental and predicted values. The present model was used to evaluate the direct interaction and quadratic effects to optimize the physico-chemical parameters for the production of bioactive metabolites that inhibit the pathogenic microorganisms measured in terms of zones of inhibition (responses). Mathematical kinetic model development and estimation of kinetic parameters also showed good approximation in terms of model fitting and regression analysis.

Deep sea sediment samples of Bay of Bengal (Visakhapatnam) have been analyzed for actinomycetes as an elite source to screen for the production of bioactive metabolites. The actinomycetes strain VSM-30 has an exciting bioactivity profile and was isolated during our systemic screening of marine actinomycetes. It was identified as Streptomyces sparsus based on morphological, physiological, biochemical, and molecular approaches. Response surface methodology regression analysis was carried out to fit the experimental data of each response by the second-order polynomial. The results have proven right interaction among process variables at optimized values of incubation time at 12 days, pH at 8, temperature at 30 °C, concentrations of starch at 1%, and tryptone at 1% and the data have been adequately fitted into the second-order polynomial models. Under these conditions, the responses (zones of inhibition) of plant pathogenic fungi Aspergillus niger, Aspergillus flavus, Fusarium oxysporum, Fusarium solani, and Penicillium citrinum were also matched with experimental and predicted results. Chemotypic analysis of ethyl acetate extract of the strain was done using LC-Q-TOF-MS revealed the presence of bioactive compounds including tryptophan dehydrobutyrine diketopiperazine, maculosin, 7-o-demethyl albocycline, albocycline M-2, and 7-o-demethoxy-7-oxo albocycline in a negative ion mode. The ethyl acetate extract of actinobacterium has been subjected to gas chromatography and mass spectroscopy (GC-MS) revealed the presence of diverse compounds such as dotriacontane, tetracosane 11-decyl-, diheptyl phthalate, 1-hexadecanesulfonyl chloride, L-alanyl-L-tryptophan, phthalic acid ethyl pentyl ester, 4-trifluoroacetoxyhexadecane, and 1H-imidazole 4,5-dihydro-2,4-dimethyl. Hence, the ethyl acetate extract of Streptomyces sparsus VSM-30 may have antibacterial, antifungal, and antioxidant activities due to the presence of secondary metabolites in ethyl acetate extract. The study also supports marine sediment samples of Bay of Bengal, a promising marine ecosystem remained to be explored for new bioactive compounds.

Objectives: To execute the influence of the physico-chemical variables on the production of the bioactive metabolites by Streptomonospra arabica VSM-25 using Response Surface Methodology. Materials and Methods: An actinobacterium strain isolated from the deep sea marine sediment samples was identified as Streptomonospra arabica VSM-25 by conventional and molecular approaches. RSM was employed to study the impact of five variables, viz. incubation time, pH, temperature, galactose and peptone concentrations on the production of antifungal metabolites by VSM-25. Growth related production formation kinetics and substrate utilization in batch system was analysed using mathematical and unstructured kinetic models. Results: Statistical study showed that the incubation time, pH, Temperature, Concentration of galactose and peptone has a significant effect (p <0.0001) on the bioactive metabolite production at their individual and interactive level. A second order polynomial model provided a satisfied fit for experimental data with regard to the production of antifungal metabolites. Maximum antimycotic activity was achieved at incubation time (11 days), pH (8), temperature (30°C), galactose concentration (2%) and peptone concentration (1%). Unstructured mathematical kinetic model was developed and estimated kinetic parameters also exhibited good approximation in terms of model fitting and regression analysis. Conclusion: To the best of our knowledge this is the first report on the production of anti fungal metabolites from S. arabica using RSM and kinetic modelling studies which firmly support the application of RSM and kinetic modelling for optimization. The study may find potential application in rapid screening and production of novel drug molecules from unexploited natural sources.Key words: Streptomonospora arabica, Antimycotic Activity, Optimization, Kinetic Modelling, Response Surface Methodology, Bioactive Metabolites. Key message: The outcome of the present study strongly supports, RSM based optimization of fermentation conditions. The optimization of environmental parameters and cultural conditions plays a crucial role in the enhanced antimycotic activity. This study contributes towards scale-up of production of antifungal agents by Streptomonospora arabica VSM-25. The unstructured model provided a better approximation of kinetic profiles of bioactive metabolite production by the strain in shake-flask fermentations. Correspondence :Prof. M. Vijayalakshmi, Department of Botany and Microbiology, Acharya Nagarjuna University, Nagarjuna nagar, Guntur-52510, Andhra Pradesh, INDIA. Phone: 9440870026Email: profmvl08@gmail.com DOI: 10.5530/jyp.2017.9.80 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

L-asparaginase is an anti-tumor enzyme and widely accepted as chemotherapeutic agent which has activity against acute lymphoblastic leukemia. The current study targets the production of L-asparaginase by Pseudonocardia endophytica VUK-10 by a statistically designed model. Experiments were performed according to central composite design of RSM with five independent variables such as time, pH, temperature, concentrations of maltose and L-asparagine concentration for optimization. All the five conditions had significant interaction with other variables for the maximum response (L-asparaginase production). Maximum L-asparaginase production was recorded as 7.42 IU/ml slightly higher than the model predicted value of 6.8 IU/ml, from statistical optimization studies. An unstructured kinetic model was proposed to depict the profiles of biomass, substrate utilization and L-asparaginase production in optimized medium under shake flask level. The logistic and Leudeking-Piret expressions were modified to predict the kinetic model parameters (µmax, X0, Xmax, α, β, γ and η) and we found that L-asparaginase production was growth-associated. High significant correlation (R 2 ) values of 0.86, 0.96 and 0.94 were observed with the experimental and predicted results for Pseudonocardia endophytica VUK-10 growth, L-asparaginase activity and Maltose utilization, respectively. The results obtained from medium optimization using RSM and unstructured mathematical models describe the L-asparaginase fermentation kinetics more effectively.

Objectives: The present work was carried out to check the capability of novel actinobacterium, Nonomuraea longicatena (VSM-16) for bioactive metabolite production and optimization of its process parameters by statistical and mathematical modeling. Methods: Response Surface Methodology (RSM) regression evaluation was done to fit the experimental data of each response with the aid of second order polynomial. Unstructured kinetic models had been developed for growth, substrate utilization and bioactive metabolite production (in terms of responses). Model based kinetic parameters were estimated and the profiles of bioactive metabolite production, substrate utilization and growth had been drawn. Results: The results have shown accurate interaction among process variables at optimized values of incubation time at 8-9 days, pH at 8-9, temperature at 30-31°C, concentrations of Mannitol at 2-2.2% and Biopeptone at 1.5-1.7% and the data have been effectively fitted into second-order polynomial models. Under these conditions, the responses (zones of inhibition) of various organisms Staphylococcus aureus, Streptococcus mutans, Xanthomonas campestris, Pseudomonas aeruginosa and Candida albicans have been also matched with experimental and predicted consequences. Conclusion: The zones of inhibition (responses) for the organisms had been also determined to be best fitted with experiment and model values.