Dynamic Optimization Physics-informed Neural Networks High-density Cultures Overflow Metabolism Hybrid Models Fed-batch Fermentation

External authors:

• Francisco Ibanez ( Pontificia Universidad Catolica de Chile )
• Hernan Puentes-Cantor ( Universidad Nacional de Colombia )
• Lisbel Barzaga-Martell ( Universidad Tecnologica Metropolitana )

Abstract:

Fed-batch cultures are the preferred operation mode for industrial bioprocesses requiring high cellular densities. Avoids accumulation of major fermentation by-products due to metabolic overflow, increasing process productivity. Reproducible operation at high cell densities is challenging (>100 gDCW/L), which has precluded rigorous model evaluation. Here, we evaluated three phenomenological models and proposed a novel hybrid model including a neural network. For this task, we generated highly reproducible fed-batch datasets of a recombinant yeast growing under oxidative, oxygen-limited, and respiro-fermentative metabolic regimes. The models were reliably calibrated using a systematic workflow based on pre-and post-regression diagnostics. Compared to the best-performing phenomenological model, the hybrid model substantially improved performance by 3.6- and 1.7-fold in the training and test data, respectively. This study illustrates how hybrid modeling approaches can advance our description of complex bioprocesses that could support more efficient operation strategies.