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Neural‐network modeling and optimization of induced foreign protein production
Author(s) -
Tholudur Arun,
Ramirez W. Fred
Publication year - 1999
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690450806
Subject(s) - artificial neural network , a priori and a posteriori , biological system , computer science , function (biology) , system dynamics , process (computing) , artificial intelligence , biology , philosophy , epistemology , evolutionary biology , operating system
An experimental verification and validation of the neural network parameter function approach to modeling dynamic systems is provided. The neural‐network parameter‐function modeling scheme utilizes some a priori process knowledge (usually material balances) and experimental data to develop a dynamic neural‐network model. Other models based on fundamental principles are also developed. The experimental system under consideration is the host‐vector system Escherichia coli D1210 and plasmid pSD8, which produces the foreign protein β‐galactosidase under the effect of the inducer IPTG. Optimal operational conditions are derived and the neural‐network‐based model is shown to better predict the dynamics and optimum for protein production than the proposed fundamental kinetic models.