Current Trends in Biotechnology and Pharmacy
Open Access
SCOPUS
  • Year: 2017
  • Volume: 11
  • Issue: 3

Optimization of Process Parameters for Bioactive Metabolite Production by Nocardiopsis trehalosi VSM-13 Using Response Surface Methodology and Unstructured Kinetic Modelling

  • Author:
  • Ushakiranmayi Managamuri1, Muvva Vijayalakshmi1,, V.S Rama Krishna Ganduri2,3, R Satish Babu4, Sudhakar Poda3
  • Total Page Count: 19
  • Page Number: 223 to 241

1Department of Botany and Microbiology, Acharya Nagarjuna University, Nagarjuna nagar, Guntur-52510, Andhra Pradesh, India

2Department of Biotechnology, K L University, Vaddeswaram, Guntur, Andhra Pradesh, India

3Department of Biotechnology, Acharya Nagarjuna University, Nagarjuna nagar, Guntur-52510, Andhra Pradesh, India

4Dept of Biotechnology, National Institute of Technology, Warangal, Telangana, India

*For Correspondence- profmvl08@gmail.com

Online published on 2 November, 2018.

Abstract

Nocardiopsis trehalosi VSM 13, an actinobacterium isolated from marine environment was tested for the optimum culture conditions in shake-flask fermentations using one-factor-at-atime method. Response Surface Methodology (RSM) based Central composite Design was used to design the experiments, build the model and determine the optimum conditions for the desirable responses. RSM using a full factorial Box-Behnken design evaluated the optimized process conditions as 10 days of incubation time, pH-8.0, temperature-35°C, fructose @ 1.5% (w/v) and yeast extract @ 1% (w/v) which influenced the bioactive metabolite production by N. trehalosi VSM 13 and RSM model obtained results (R2 > 0.99) revealed a satisfactory correlation between the experimental and predicted values. Unstructured kinetic model-based parameters were also evaluated using non-linear regression method to test the fitness of the selected models. Parameters of growth and substrate utilization rates have shown excellent significant R2 values of 0.996 and 0.994, respectively.

Keywords

Nocardiopsis trehalosi, Bioactive metabolites, Regression analysis, Statistical optimization