Comparing kinetic versus stoichiometric priorities in hybrid models of CHO metabolism

Understanding Chinese hamster ovary (CHO) cell metabolism through mathematical models is essential for optimizing culture media and biomanufacturing processes. Stoichiometric models estimate intracellular fluxes under steady state, while kinetic models capture dynamic behavior but often for a limited number of reactions; dynamic metabolic flux analysis (dMFA) combines both approaches in a hybrid framework, though challenges remain in applying such hybrid frameworks to bioprocesses. Here, we enhanced an existing CHO-metabolism model by incorporating ¹³C-labeled data to refine kinetic expressions and stoichiometric constraints in key pathways, like the asparagine-aspartate network and serine biosynthesis. A novel kinetic-oriented modeling framework (KOM) which emphasizes kinetic expressions, was introduced and compared against the stoichiometric-oriented model (SOM) which prioritizes the pseudo steady state assumption (PSSA). Across two industrially relevant fed-batch CHO culture conditions with varying initial concentrations of nutrients, the KOM outperformed the SOM in predicting viable cell density, antibody production, and multiple amino acids and metabolites. Indeed, the KOM was able to predict production-to-consumption shifts in lactate and alanine, fluctuations in ammonia, and dynamics of amino acids like asparagine and the serine-glycine pool. The KOM also showed better performance under lactate supplementation, with slight parameter adjustments helping to improve model fidelity, likely due to the impact of high lactate on antibody and VCD. Our findings demonstrate that hybrid models emphasizing empirical kinetics over strict pseudo-steady-state constraints capture biologically realistic dynamics, and also produce parameters useful across varying conditions, making them a practical and powerful tool for characterizing CHO cell culture performance in the future.