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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-180962 |
| Date | January 2021 |
| Creators | Nordström, Frida |
| Publisher | Umeå universitet, Institutionen för fysik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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