Applying neural networks as software sensors for enzyme engineering.
The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.
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Laboratory of Bioprocess Engineering, Helsinki University of Technology, PO Box 6100, FIN-02015 HUT, Finland.
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D000465:Algorithms; D001709:Biotechnology / Q000379:methods*; D004798:Enzymes / Q000235:genetics* / Q000378:metabolism; D005285:Fermentation; D005087:Glucan 1,4-alpha-Glucosidase / Q000235:genetics / Q000378:metabolism; D008049:Lipase / Q000235:genetics / Q000378:metabolism; D016571:Neural Networks, Computer; D015202:Protein Engineering / Q000379:methods*; D011994:Recombinant Proteins / Q000235:genetics / Q000378:metabolism; D012984:Software; D043325:Xylan Endo-1,3-beta-Xylosidase; D014995:Xylosidases / Q000235:genetics / Q000378:metabolism; D000516:alpha-Amylases / Q000235:genetics / Q000378:metabolism; D001616:beta-Galactosidase / Q000235:genetics / Q000378:metabolism
D004798:Enzymes; D011994:Recombinant Proteins; D008049:Lipase; D014995:Xylosidases; D000516:alpha-Amylases; D001616:beta-Galactosidase; D005087:Glucan 1,4-alpha-Glucosidase; D043325:Xylan Endo-1,3-beta-Xylosidase