Optimization of a fermentation medium using neural networks and genetic algorithms.
Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.
Nagata
Yuko
Y
Department of Chemical and Process Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
Chu
Khim Hoong
KH
D000465:Algorithms; D000581:Amidohydrolases / Q000096:biosynthesis*; D019149:Bioreactors / Q000382:microbiology*; D018929:Cell Culture Techniques / Q000379:methods*; D002455:Cell Division / Q000502:physiology; D003198:Computer Simulation; D003470:Culture Media / Q000378:metabolism*; D005246:Feedback / Q000502:physiology; D005285:Fermentation / Q000502:physiology; D008954:Models, Biological; D016571:Neural Networks, Computer; D015202:Protein Engineering / Q000379:methods; D011786:Quality Control; D012231:Rhizobium / Q000166:cytology / Q000254:growth & development* / Q000378:metabolism*
D003470:Culture Media; D000581:Amidohydrolases; C020047:dihydropyrimidinase