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Enhancing Biosurfactant Production by Hypersaline Bacillus amyloliquefaciens SK27 using Response Surface Methodology and Genetic Algorithm


Affiliations
1 Department of Biotechnology, Goa University, Goa 403 206, India
 

The use of biosurfactants has been limited because of their low yield and high production cost. A central composite design was used to study the interactive effect of sucrose, yeast extract and sodium chloride which were the most influencing variables. Response surface analysis showed that the quadratic model with R2 value of 0.9983 was fit for biosurfactant production. When genetic algorithm was used for maximization, the optimal activity (oil displacement zone) was found close to that obtained by response surface methodology, both of which were close to the predicted value. Biosurfactant production was enhanced by 1.2- fold using these approaches.

Keywords

Bacillus amyloliquefaciens, Biosurfactants, Central Composite Design, Genetic Algorithm, Response Surface Methodology.
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  • Karnath, N. G. K., Deo, P. G. and Veenanadig, N. K., Microbial production of biosurfactant and their importance. Curr. Sci., 1999, 77, 116–123.

  • Khopade, A., Ren, B., Liu, X. Y., Mahadik, K., Zhang, L. and Kokare, C., Production and characterization of biosurfactant from marine Streptomyces sp. B3. J. Colloid Interface Sci., 2012, 367, 311–318.

  • Satpute, S. K., Banpurkar, A. G., Dhakephalkar, P. K., Banat, I. M. and Chopade, B. A., Methods of investigating biosurfactants and bioemulsifiers: a review. Crit. Rev. Biotechnol., 2010, 30, 127–144; doi:10.3109/07388550903427280.

  • Mukherjee, S., Das, P. and Sen, R., Towards commercial production of microbial surfactants. Trends Biotechnol., 2006, 24(11), 509–515; doi:10.1016/j.tibtech.2006.09.005.

  • Mukherjee, S., Das, P., Sivapathasekaran, C. and. Sen, R., Enhanced production of biosurfactant by a marine bacterium on statistical screening of nutritional parameters. Biochem. Eng. J., 2008, 42, 254–260.

  • Kim, B. and Kim, J., Optimization of culture conditions for the production of biosurfactant by Bacillus subtilis JK-1 using response surface methodology. J. Kor. Soc. Appl. Biol. Chem., 2013, 56, 279−287; doi:10.1007/s13765-013-3044-6.

  • Singh, V., Haque, S., Niwas, R., Srivastava, A., Pasupuleti, M. and Tripathi, C. K. M., Strategies for fermentation medium optimization: an in depth review. Front. Microbiol., 2017, 7, 2087; doi:10.3389/fmicb.2016.02087.

  • Alikunju, A. P. et al., A statistical approach to optimize cold active β -Galactosidase production by an arctic sediment pscychrotrophic bacteria. Enterobacter ludwigii (MCC 3423) in cheese whey. Catal. Lett., 2018, 148(2), 712–724; doi:org/10.1007/s10562-017-2257-4.

  • Myers, R. H. and Montgomery, D. C., Response Surface Methodology: Product and Process Optimization Using Designed Experiments, New York, John Wiley, USA, 2002, 2nd edn.

  • Vohra, A. and Satyanarayana, T., Statistical optimization of the medium components by response surface methodology to enhance phytase production by Pichia anomala. Process Biochem., 2002, 37, 999–1004.

  • Liyana-Pathirana, C. and Shahidi, F., Optimization of extraction of phenolic compounds from wheat using response surface methodology. Food Chem., 2005, 93, 47–56.

  • Montgomery, D., Design and Analysis of Experiments, John Wiley, New York, USA, 2005, 6th edn.

  • McCall, J., Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math., 2005, 184(1), 205–222; doi:10.1016/j.cam.2004.07.034.

  • Gaitonde, V. N., Karnik, S. R., Achyutha, B. T. and Siddeswarappa, B., GA application to RSM based models for burr size reduction in drilling. J. Sci. Indust. Res., 2005, 64, 347.

  • Franco-Lara, E., Link, H. and Weuster-Botz, D., Evaluation of artificial neural network for modelling and optimization of medium composition with a genetic algorithm. Proc. Biochem., 2006, 41, 2200–2206.

  • Moorthy, I. M. G. and Baskar, R., Statistical modeling and optimization of alkaline protease production from a newly isolated alkalophilic Bacillus species BGS using response surface methodology and genetic algorithm. Prep. Biochem. Biotechnol., 2013, 43, 293–314.

  • Fernandes, S., Kerkar, S., Leitao, J. and Mishra, A., Probiotic role of salt pan bacteria in enhancing the growth of Whiteleg Shrimp, Litopenaeus vannamei. Probiot. Antimicrob. Proteins, 2019, 1–15; doi.org/10.1007/s12602-018-9503-y.

  • Zhang, W., Zhang, X. and Cui, H., Isolation, fermentation optimization and performance studies of a novel biosurfactant producing strain Bacillus amyloliquefaciens, Chem. Biochem. Eng. Q., 2015, 29(3), 447–456.

  • Thavasi, R., Sharma, S. and Jayalakshmi, S., Evaluation of screening methods for the isolation of biosurfactant producing marine bacteria. J. Pet. Environ. Biotechnol., 2011, S1(001), 1–6; doi:10.4172/2157-7463.S1-001.

  • Morikawa, M., Daido, H., Takao, T., Murata, S., Shimonishi, Y. and Imanaka, T., A new lipopeptide biosurfactant produced by Arthrobacter sp. strain MIS 38. J. Bacteriol., 1993, 175, 6459– 6466.

  • Youssef, N. H., Duncan, K. E., Nagle, D. P., Savage, K. N., Knapp, R. M. and McInerney, M. J., Comparison of methods to detect biosurfactant production by diverse microorganisms. J. Microbiol. Meth., 2004, 56, 339–347; doi:10.1016/j.mimet.2003.11.001.

  • Irfan, M., Nadeem, M. and Syed, Q., One-factor-at-a-time (OFAT) optimization of xylanase production from Trichoderma virideIR05 in solid-state fermentation. J. Radiat. Res. Appl. Sci., 2014, 7, 317–326; doi:10.1016/j.jrras.2014.04.004.

  • Fernandes, M. S. and Kerkar, S., Enhancing the anti-tyrosinase activity of a hypersaline Kitasatospora sp. SBSK430 by optimizing the medium components. Curr. Sci., 2019, 116(4), 649–653; doi:10.18520/cs/v116/i4/649-653.

  • Lee, N. K., Statistical optimization of medium and fermentation conditions of recombinant Pichia pastoris for the production of xylanase. Biotechnol. Bioprocess Eng., 2018, 23(1), 55–63; doi:10.1007/s12257-017-0262-5.

  • Srivastava, A. et al., Response surface methodology-genetic algorithm based medium optimization, purification, and characterization of cholesterol oxidase from Streptomyces rimosus. Sci. Rep., 2018, 8(1), 10913

  • Anjum, M. F., Tasadduq, I. and Al-Sultan, K., Response surface methodology: a neural network approach. Eur. J. Oper. Res., 1997, 101, 65–73.

  • Bas, D. and Boyac, I. S., Modeling and optimization I: usability of response surface methodology. J. Food Eng., 2007, 78, 836– 845.

  • Akhnazarova, S. and Kefarov, V., Experiment Optimization in Chemistry and Chemical Engineering, Mir Publishers, Moscow, Russia, 1982.

  • Zhu, Z., Zhang, G., Luo, Y., Ran, W. and Shen, Q., Production of lipopeptides by Bacillus amyloliquefaciens XZ-173 in solid state fermentation using soybean flour and rice straw as the substrate. Bioresour. Technol., 2012, 112, 254–260; doi:10.1016/j.biortech.2012.02.057.

  • Box, G. E. and Wilson, K., On the experimental attainment of optimum conditions. J. R. Stat. Soc. Series. B, 1951, 13, 1–45.

  • Saibaba, K. V. N. and King, P., Modelling and optimization of dye removal process using hybrid response surface methodology and genetic algorithm approach. J. Fundam. Renew. Energy Appl., 2014, 4(01), doi:10.4172/2090-4541.1000126.

  • Shirodkar, P. V. and Muraleedharan, U. D., Enhanced α-amylase production by a marine protist, Ulkenia sp. using response surface methodology and genetic algorithm. Prep. Biochem. Biotechnol., 2017, 47(10), 1043–1049; doi:10.1080/10826068.


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  • Enhancing Biosurfactant Production by Hypersaline Bacillus amyloliquefaciens SK27 using Response Surface Methodology and Genetic Algorithm

Abstract Views: 225  |  PDF Views: 66

Authors

Ruchira Malik
Department of Biotechnology, Goa University, Goa 403 206, India
Savita Kerkar
Department of Biotechnology, Goa University, Goa 403 206, India

Abstract


The use of biosurfactants has been limited because of their low yield and high production cost. A central composite design was used to study the interactive effect of sucrose, yeast extract and sodium chloride which were the most influencing variables. Response surface analysis showed that the quadratic model with R2 value of 0.9983 was fit for biosurfactant production. When genetic algorithm was used for maximization, the optimal activity (oil displacement zone) was found close to that obtained by response surface methodology, both of which were close to the predicted value. Biosurfactant production was enhanced by 1.2- fold using these approaches.

Keywords


Bacillus amyloliquefaciens, Biosurfactants, Central Composite Design, Genetic Algorithm, Response Surface Methodology.

References





DOI: https://doi.org/10.18520/cs%2Fv117%2Fi5%2F847-852