An integrated identifiability and metabolic flux analysis workflow for reducing and repairing dynamic CHO metabolic models

Over-parametrized kinetic-metabolic models can fit available data accurately yet still yield unreliable parameter estimates and output uncertainties. We developed an integrated workflow that combines Fisher information matrix-based identifiability analysis diagnostics, uncertainty propagation, and metabolic flux analysis (MFA) to reduce model size and assess the transferability of a kinetically constrained dynamic flux balance analysis (dFBA) model of CHO perfusion culture. Using Control medium data, the dFBA model size was reduced from 48 to 43 kinetic parameters while maintaining performance across 23 measured extracellular species. The reduced model’s transferability was assessed by predicting cell behavior in an amino acid-supplemented medium (Medium C), which lowered the specific growth rate while increasing specific productivity. The reduced model successfully predicted viable cell density, antibody titer, and supplemented amino acids for Medium C but failed to capture the reversal of glutamate metabolism and the increase in lactate production. Output uncertainty quantification identified structural gaps rather than parameter uncertainty as the underlying cause of prediction failures, with 79% of Medium C experimental points lying outside the 95% parameter-propagated interval. Stoichiometric MFA linked the Medium C phenotype to increased amino acid catabolism, identified the intracellular sources for glutamate and lactate production, and enabled estimation of two new rate-constants for their production directly from stoichiometric flux, rather than fitted to data. MFA-guided repair reduced glutamate and lactate NRMSE in Medium C by 64% and 36%, respectively. The workflow provides a systematic strategy for improving model identifiability and using MFA to diagnose and repair structural discrepancies.