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KMID : 0545120120220070939
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Journal of Microbiology and Biotechnology
2012 Volume.22 No. 7 p.939 ~ p.946
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Enhanced Antibiotic Production by Streptomyces sindenensis Using Artificial Neural Networks Coupled with Genetic Algorithm and Nelder-Mead Downhill Simplex
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C. K. M. Tripathi
Mahvish Khan
Vandana Praveen
Saif Khan
Akanksha Srivastava
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Abstract
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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 μg/ml, which nearly doubled (176 μg/ml) with the ANN-GA optimization. ANN-NMDS optimization was found to be more efficacious, and maximum antibiotic production (197 μg/ml) was obtained by cultivating the cells with (g/l) fructose 2.7602, MgSO4 1.2369, (NH4)2PO4 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.
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KEYWORD
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Streptomyces sindenensis, Antibiotic, Artificial neural networks, Genetic algorithm, Nelder mead downhill simplex
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