Abstract
An artificial neural network (ANN) modeling scheme was constructed for the identification of both recombinant tissue-type plasminogen activator (r-tPA) protein production and glycosylation from Chinese hamster ovary (CHO) cell culture, cultivated in a stirred bioreactor. A series of hybrid feed-forward backpropagation neural networks were constructed to function as a software sensor over a wide range of shear-induced culture states. This enabled predictions of ammonia production, viable cell density, r-tPA production and glycosylation. The sensor was based on an initial input vector space consisting of simple metabolite concentrations, batch cultivation time, and a description of shear stress applied to the culture.
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