A-S.B.52: Assessing and Validating Flux Predictions in CHO Genome-Scale Metabolic Models
Genome-scale metabolic models provide a critical link between predictive modeling and experimental bioprocess design. However, options for rigorous model validation are currently limited. Here, we propose an approach for validating models against experimentally measured exchange fluxes, previously published 13C metabolic fluxes, and experimentally measured protein abundances.
We analyzed two genome-scale CHO cell models (iCHO1766 and iCHO3K) constrained with experimentally measured, condition- and time-specific exchange fluxes, and applied flux balance analysis and parsimonious FBA with biomass maximization. Model validation was performed using three complementary approaches: comparison of predicted and measured exchange fluxes via selective constraint relaxation, validation of intracellular fluxes against published 13C metabolic fluxes, and evaluation of predicted fluxes using condition- and time-specific proteomics data.
Using the selective relaxation approach, iCHO1766 predicted 9 out of 22 measured exchange fluxes with R2 ≥ 0.75, exhibiting strong correlation with measured fluxes. Application of parsimonious FBA substantially improved prediction performance, with 16 out of 22 exchange fluxes achieving the same threshold. Nevertheless, specific reaction fluxes (lactate, glycine, and glutamic acid exchange fluxes) were poorly predicted across both approaches and were consistently predicted as zero, indicating limitations in model structure or objective formulation.
In summary, genome-scale CHO cell models provide a powerful framework for predicting metabolic behavior and guiding bioprocess design. iCHO1766 achieved strong correlation with measured exchange fluxes, and parsimonious FBA further improved predictions. Model-dependent performance highlights opportunities to refine larger models like iCHO3K. Ongoing validation with intracellular 13C fluxes and proteomics will enhance prediction reliability for optimizing culture conditions and metabolic interventions.
Co-authors: Larissa Hofer, Jerneja Stor, Thomas Rauter, Thomas Berger, Dominik Hofreither, Laura Liesinger, Wolfgang Esser-Skala, Ruth Birner-Gruenberger, Veronika Schäpertöns, Nicole Borth, Christian Huber
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