@article{6102,
  keywords = {Actinomycetales, Algorithms, Artificial Intelligence, Bioreactors, Cell Culture Techniques, Culture Media, Decision Support Techniques, Fermentation, Models, Biological, Rifamycins},
  author = {Bapat P and Wangikar PP},
  title = {Optimization of rifamycin B fermentation in shake flasks via a machine-learning-based approach.},
  abstract = {<p>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 are 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.</p>},
  year = {2004},
  journal = {Biotechnology and bioengineering},
  volume = {86},
  pages = {201-8},
  month = {2004 Apr 20},
  issn = {0006-3592},
  doi = {10.1002/bit.20056},
  language = {eng},
}