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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Preliminary Screening Of Media Formulation Through One Variable At A Time Methodology

[Full Text]



Rajesh Singh Tomar, Neha Sharma



One variable analysis, Medium Optimization, Response Surface Methodology, Altered Medium, Biomass.



Optimization of media component through changes in one variable at a time was used to enhance the biomass and antioxidant activity of selected microorganism (Escherichia coli MTCC no. 40) on different formulated media. For the present study, different types of carbon, nitrogen and mineral sources were used to formulate media composition. On the basis of absorbance, components were selected to formulate new medium and finally formulate a new medium by using all variables in one medium. It was reported that unaltered media showed 0.126 0.001 absorbance, media 1 (altered carbon source) showed 0.143 0.001, media 2 (altered nitrogen source) showed 0.150 0.001, media 3 (altered mineral source) showed 0.124 0.012 and media 4 (Altered medium) showed 0.090 0.005 absorbance at 492 nm wavelength. The component comparison and analysis was based on changes in one factor in medium. Significant result i.e. increase in biomass was reported in altered nitrogen and carbon medium compared to the unaltered medium. These components will be further used in the formulation and optimization of medium component for response surface methodology which are the primary steps involved in bioprocess technology to enhance the biomass of the particular microorganism.



[1]. Aneja KR. 2015. Experiments in microbiology, plant pathology and biotechnology. New age international (P) limited: ISBN: 978-81-224-1494-3.
[2]. Dasari VRRK, Donthireddy SRR, Nikku MY. 2009. Optimization of medium constituents for Cephalosporin C production using response surface methodology and artificial neural networks. J Biochem Technol. 1: 6974.
[3]. Franco-Lara E, Link H, Weuster-Botz D. 2006. Evaluation of artificial neural networks for modelling and optimization of medium composition with a genetic algorithm. Process Biochem.41, 22002206. 10.1016/j.procbio.2006.06.024.
[4]. Gupte M, Kulkarni P. 2003. A study of antifungal antibiotic production by Thermomonospora sp MTCC 3340 using full factorial design. J. Chem. Technol. Biotechnol.78, 605610. 10.1002/jctb.818
[5]. Mohamed MS, Tan JS, Mohamad R. 2013. Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetr aselmis sp. FTC209 grown under mixotrophic condition. Sci World J.114.
[6]. Mohammed N, Baeshen1, Ahmed M, Al-Hejin1, Roop S, Mohamed MM, Hassan AI, Kulvinder S, Nabih A. 2015. Production of Biopharmaceuticals in E. coli: Current Scenario and Future Perspectives J. Microbiol. Biotechnol. 25(7), 953962 http://dx.doi.org/10.4014/jmb.1412.12079
[7]. Montgomery D.C., Design and Analysis of Experiments.1991. New York: John Wiley and Sons Inc.
[8]. Myers RH, Montgomery DC. 1995. New York: Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons.
[9]. Oh S, Rheem S, Sim J, Kim S, Baek Y. 1995. Appl. Environ. Microbiol. 61 3809-3814.
[10]. Parra R, David A, Naresh M.2005. Medium optimization for the production of the secondary metabolite squalestatin S1 by a Phoma species combining orthogonal design and response surface methodology. Enzyme Microbiol Technol. 37: 704711.
[11]. Tomar RS, Sharma NS, Banerjee S. 2019. Pre optimization and one factor analysis of media composition for Response surface methodology. Journal of Current Science, Vol 20, Special Issue. 04. Available Online: https://journal.scienceacad.com.
[12]. Rao JK, Chul-Ho K, Sang-Ki R. 2000. Statistical optimization of medium for the production of recombinant hirudin from Saccharomyces cerevisiae using response surface methodology. Process Biochem. 35:639647.
[13]. Rodriguez V, Asenjo JA, Andrews BA. 2014. Design and implementation of a high yield production system for recombinant expression of peptides. Microb. Cell Fact. 13: 65.
[14]. Rogosa M, Franklin JG, Perry KD. 1961 J. Gen. Microbiol. 24 .473-482.
[15]. Sahdev S, Khattar SK, Saini KS. 2008. Production of active eukaryotic proteins through bacterial expression systems: a review of the existing biotechnology strategies. Mol. Cell. Biochem. 307: 249-264.
[16]. Schmidt FR. 2005. Optimization and scale up of industrial fermentation processes. Appl Microbiol Biotechnol. 68:425435.
[17]. Tan JS, Ramanan RN, Ling TC. 2010. Comparative of predictive capabilities of response surface methodology and artificial neural network for optimization of periplasmic interferon-a2b production by recombinant Escherichia coli.Minerva Biotechnol. 22:63- 73.
[18]. Wang JC, Hu SH, Liang ZC, Yeh CJ. 2005. Optimization for the production of water- soluble polysaccharide from Pleurotus citrinopileatus in submerged culture and its antitumor effect. Appl. Microbiol. Biotechnol. 67, 759766. 10.1007/s00253-004-1833-x
[19]. Xu CP, Kim SW, Hwang HJ, Choi JW, Yun JW. 2003. Optimization of submerged culture conditions for mycelia growth and exobiopolymer production by Paecilomyces tenuipes C240. Process Biochem. 38:10251030