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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/9486

Title: Optimization of Rifamycin B fermentation in shake flasks via a machine-learning-based approach
Authors: BAPAT, PM
WANGIKAR, PP
Keywords: response-surface methodology
amycolatopsis-mediterranei
3-amino-5-hydroxybenzoic acid
genetic algorithms
biosynthesis
lovastatin
expression
culture
Issue Date: 2004
Publisher: JOHN WILEY & SONS INC
Citation: BIOTECHNOLOGY AND BIOENGINEERING, 86(2), 201-208
Abstract: Rifamycin B is an important polyketide antibiotic used in the treatment of tuberculosis and leprosy. We present results on medium optimization for Rifamycin B production via a barbital insensitive mutant strain of Amycolatopsis mediterranei S699. Machine-learning approaches such as Genetic algorithm (GA), Neighborhood analysis (NA) and Decision Tree technique (DT) were explored for optimizing the medium composition. Genetic algorithm was applied as a global search algorithm while NA was used for a guided local search and to develop medium predictors. The fermentation medium for Rifamycin B consisted of nine components. A large number of distinct medium compositions Eire possible by variation of concentration of each component. This presents a large combinatorial search space. Optimization was achieved within five generations via GA as well as NA. These five generations consisted of 178 shake-flask experiments, which is a small fraction of the search space. We detected multiple optima in the form of 11 distinct medium combinations. These medium combinations provided over 600% improvement in Rifamycin B productivity. Genetic algorithm performed better in optimizing fermentation medium as compared to NA. The Decision Tree technique revealed the media-media interactions qualitatively in the form of sets of rules for medium composition that give high as well as low productivity. (C) 2004 .
URI: http://dx.doi.org/10.1002/bit.20056
http://dspace.library.iitb.ac.in/xmlui/handle/10054/9486
http://hdl.handle.net/10054/9486
ISSN: 0006-3592
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