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|Title: ||Optimization of Rifamycin B fermentation in shake flasks via a machine-learning-based approach|
|Authors: ||BAPAT, PM|
|Keywords: ||response-surface methodology|
|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 .|
|Appears in Collections:||Article|
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