Journal of Microbiology and Biotechnology

The Korean Society for Microbiology and Biotechnology (한국미생물·생명공학회)
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Enhanced Antibiotic Production by Streptomyces sindenensis Using Artificial Neural Networks Coupled with Genetic Algorithm and Nelder-Mead Downhill Simplex

  • Tripathi, C.K.M. (Division of Fermentation Technology, C.S.I.R., Central Drug Research Institute) ;
  • Khan, Mahvish (Department of Biotechnology, Integral University) ;
  • Praveen, Vandana (Division of Fermentation Technology, C.S.I.R., Central Drug Research Institute) ;
  • Khan, Saif (Department of Biotechnology, Integral University) ;
  • Srivastava, Akanksha (Division of Fermentation Technology, C.S.I.R., Central Drug Research Institute)
  • Received : 2011.09.14
  • Accepted : 2012.03.15
  • Published : 2012.07.28
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Abstract

Antibiotic production with Streptomyces sindenensis MTCC 8122 was optimized under submerged fermentation conditions by artificial neural network (ANN) coupled with genetic algorithm (GA) and Nelder-Mead downhill simplex (NMDS). Feed forward back-propagation ANN was trained to establish the mathematical relationship among the medium components and length of incubation period for achieving maximum antibiotic yield. The optimization strategy involved growing the culture with varying concentrations of various medium components for different incubation periods. Under non-optimized condition, antibiotic production was found to be $95{\mu}g/ml$, which nearly doubled ($176{\mu}g/ml$) with the ANN-GA optimization. ANN-NMDS optimization was found to be more efficacious, and maximum antibiotic production ($197{\mu}g/ml$) was obtained by cultivating the cells with (g/l) fructose 2.7602, $MgSO_4$ 1.2369, $(NH_4)_2PO_4$ 0.2742, DL-threonine 3.069%, and soyabean meal 1.952%, for 9.8531 days of incubation, which was roughly 12% higher than the yield obtained by ANN coupled with GA under the same conditions.

Keywords

References

  1. Box, G. E. P., W. G. Hunter, and J. S. Hunter. 1978. Statistics for Experiments. John Willey & Sons, New York.
  2. Castro, P. M. L., P. M. Hayter, A. P. Ison, and A. T. Bull. 1992. Application of a statistical design to the optimization of culture medium for recombinant interferon-gamma production by Chinese hamster ovary cells. Appl. Microbiol. Biotechnol. 38: 84-90.
  3. Chhatpar, H. S., R. Vaidya, and P. Vyas. 2003. Statistical optimization of medium components for the production of chitinase by Alcaligenes xylosoxydans. Enzyme Microb. Technol. 33: 92-96. https://doi.org/10.1016/S0141-0229(03)00100-5
  4. Dennis, J. E. and R. B. Chnabel. 1983. Numerical Methods For Unconstrained Optimization and Nonlinear Equations. Prentice- Hall Press, Englewood Cliffs, NJ.
  5. Fleck-Schneidera, P., F. Lehra, and C. Posten. 2007. Modelling of growth and product formation of Porphyridium purpureum. J. Biotechnol. 132: 134-141. https://doi.org/10.1016/j.jbiotec.2007.05.030
  6. Furuhashi, K. and M. Takagi. 1984. Optimization of a medium for the production of 1,2-epoxytetradecane by Nocardia corallina B- 276. Appl. Microbiol. Biotechnol. 20: 6-9.
  7. Gill, P. E., W. Murray, and M. H., Wright. 1981. Practical Optimization. Academic Press, New York.
  8. Gough, S., O. Flynn, C. J. Hack, and R. Marchant. 1996. Fermentation of molasses using a thermotolerant yeast, Kluyveromyces marxianus IMB3: Simplex optimization of media supplements. Appl. Microbiol. Biotechnol. 46: 187-190.
  9. Haider, M. A., K. Pakshirajan, A. Singh, and S. Chaudhary. 2008. Artificial neural network-genetic algorithm approach to optimize media constituents for enhancing lipase production by a soil microorganism. Appl. Biochem. Biotechnol. 144: 225-235. https://doi.org/10.1007/s12010-007-8017-y
  10. Inbar, I. and A. Lapidot. 1988. Metabolic regulation in Streptomyces parvulus during actinomycin D synthesis, studied with $^{13}C$- and 1$^{15}N$-labeled precursors by $^{13}C$ and $^{15}N$ nuclear magnetic resonance spectroscopy and by gas chromatographymass spectrometry. J. Bacteriol. 170: 4055-4064.
  11. Jacoby, S. L. S., J. S. Kowalik, and J. T. Pizzo. 1972. Iterative Methods for Nonlinear Optimization Problems. Prentice-Hall Press, Englewood Cliffs, NJ.
  12. Katz, E., P. Pienta, and A. Sivak. 1956. The role of nutrition in the synthesis of actinomycin. Appl. Microbiol. 6: 236-241.
  13. Khan, S., V. Bhakuni, R. Tewari, C. K. M. Tripathi, and V. D. Gupta. 2010. Maximizing the native concentration and shelf life of a protein: A multi objective optimization to reduce aggregation. Appl. Microbiol. Biotechnol. 89: 99-108.
  14. Klein, E. J., S. L. Rivera, and J. E. Porter. 2000. Optimization of ion-exchange protein separations using a vector quantizing neural network. Biotechnol. Prog. 16: 506-512. https://doi.org/10.1021/bp000011w
  15. Nagata, Y. and K. H. Chu. 2003. Optimization of a fermentation medium using neural networks and genetic algorithms. Biotechnol. Lett. 25: 1837-1842. https://doi.org/10.1023/A:1026225526558
  16. Nelder, J. A. and R. Mead. 1965. A simple method for function minimization. Comp. J. 7: 308-313. https://doi.org/10.1093/comjnl/7.4.308
  17. Polak, E. 1971. Computational Methods in Optimization. Academic Press, New York.
  18. Powell, M. J. D. 1964. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comp. J. 7: 155-162. https://doi.org/10.1093/comjnl/7.2.155
  19. Praveen, V., C. K. M. Tripathi, V. Bihari, and S. C. Srivastava. 2008. Production of actinomycin-D by a new isolate, Streptomyces sindenensis. Ann. Microbiol. 58: 109-114. https://doi.org/10.1007/BF03179453
  20. Pundle, A. V. and H. S. Raman. 1994. Medium optimization for the production of penicillin V acylase from Bacillus sphaericus. Biotechnol. Lett. 16: 1041-1046. https://doi.org/10.1007/BF01022400
  21. Schmidt, F. R. 2005. Optimization and scale up of industrial fermentation processes. Appl. Microbiol. Biotechnol. 68: 425-435. https://doi.org/10.1007/s00253-005-0003-0
  22. Silveira, R. G., T. Kakizono, S. Takemoto, N. Nishio, and S. Nagai. 1991. Medium optimization by an orthogonal design for the growth of Methanosarcina barkeri. J. Ferm. Bioeng. 72: 20-25. https://doi.org/10.1016/0922-338X(91)90140-C
  23. Singh, V., M. Khan, S. Khan, and C. K. M. Tripathi. 2009. Optimization of actinomycin V production by Streptomyces triostinicus using artificial neural network and genetic algorithm. Appl. Microbiol. Biotechnol. 82: 379-385. https://doi.org/10.1007/s00253-008-1828-0
  24. Sousa, M. F. V. Q., C. E. Lopes, and N. Jr. Pereira. 2002. Development of a bioprocess for the production of actinomycin-D. Braz. J. Chem. Eng. 19: 277-285.
  25. Spendley, W., G. R. Hext, and F. R. Himsworth. 1962. Sequential application of simplex designs in optimization and evolutionary operation. Technometrics 4: 441-461. https://doi.org/10.1080/00401706.1962.10490033
  26. Thiel, T., J. Bramble, and S. Rogers. 1989. Optimum conditions for growth of cyanobacteria on solid media. FEMS Microbiol. Lett. 61: 27-31. https://doi.org/10.1111/j.1574-6968.1989.tb03546.x
  27. Windsor, S. A. M. and H. Tinker. 1996. Binding of biologically important molecules to DNA, probed using electro-fluorescence polarization spectroscopy. Biophys. Chem. 58: 141-150. https://doi.org/10.1016/0301-4622(95)00093-3
  28. Yarbro, L. and A. S. N. Deming. 1974. Selection and preprocessing of factors for simplex optimization. Anal. Chim. Acta 73: 391-398. https://doi.org/10.1016/S0003-2670(01)85476-3

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