A general artificial neural network for the modelization of culture kinetics of different CHO strains

Animal cell cultures are characterized by very complex nonlinear behaviors, difficult to simulate by analytical modeling. Artificial Neural Networks, while being black box models, possess learning and generalizing capacities that could lead to better results. We first trained a three-layer perceptro...

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Bibliographic Details
Published in:Cytotechnology (Dordrecht) 2001-07, Vol.36 (1-3), p.55-60
Main Authors: MARIQUE, T, CHERLET, M, HENDRICK, V, GODIA, F, KRETZMER, G, WERENNE, J
Format: Article
Language:English
Subjects:
ammonia
Biological and medical sciences
Biotechnology
Cell culture
Cell lines
Fundamental and applied biological sciences. Psychology
glucose
glutamine
Neural networks
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Summary:Animal cell cultures are characterized by very complex nonlinear behaviors, difficult to simulate by analytical modeling. Artificial Neural Networks, while being black box models, possess learning and generalizing capacities that could lead to better results. We first trained a three-layer perceptron to simulate the kinetics of five important parameters (biomass, lactate, glucose, glutamine and ammonia concentrations) for a series of CHO K1(Chinese Hamster Ovary, type K1) batch cultures. We then tried to use the same trained model to simulate the behavior of recombinant CHO TF70R. This was achieved, but necessitated to synchronize the time-scales of the two cell lines to compensate for their different specific growth rates.
ISSN:0920-9069
1573-0778
DOI:10.1023/a:1014084802708