Using Neural Networks to Predict Cell Specific Productivity in Bioreactors

Sammanfattning: During production of certain biopharamaceutical drugs, cells are grown in a liquid mediainside bioreactors with the goal of producing a specic biomaterial that can be rened intoa drug. This project investigates whether the use of Neural Networks (NN) can decreasethe prediction error, in terms of Mean Squared Error (MSE), for 2 metabolic processes incells compared to current methods. The rst experiment tests predictions of cell-SpecicConsumption Rate (SCR) of 5 dierent metabolites and the second experiment testspredictions of cell-Specic Production Rate (SPR) of titer. Fully connected feed-forwardneural networks were trained and cross-validation was used to obtain MSE betweenpredictions and measured values. The SCR predictions made by the NN was better thanthe original model predictions for all 5 metabolites. The predictions of SPR from the NNcannot with certainty be said to be better than the original model, with a p-value of 0.13.These results indicate that using NNs when modeling cell metabolism in bioreactors candecrease its prediction error, leading to better control of the bioreactor environment andmore ecient production.