Enzyme capacity-based genome scale modelling of CHO cells

Chinese hamster ovary (CHO) cells are widely used in biopharmaceutical production. Existing genome-scale models help identify metabolic bottlenecks but struggle to predict intracellular fluxes accurately. Prior research has shown that flux balance analysis lacks constraints to account for enzyme capacities. This gap motivated the integration of enzyme kinetics into metabolic models. No prior work had resolved how enzyme limitations affect pathway usage. Understanding clonal differences remains a challenge in bioprocessing. Metabolic models often fail to capture overflow metabolism accurately. Incorporating enzyme capacity could improve model reliability.

The aim of this work is to enhance genome-scale modeling of CHO cells by integrating enzyme capacity constraints. The study addresses the challenge of predicting intracellular fluxes under varying bioprocessing conditions. Researchers sought to improve flux balance analysis by incorporating enzyme kinetics. The motivation stems from the need to better understand metabolic pathway usage. The approach focuses on reducing flux variability in a biologically meaningful way. The goal is to capture overflow metabolism under glucose excess conditions. This effort supports the development of digital twin models for biomanufacturing. The study aims to guide cell engineering and process optimization.

The updated CHO genome-scale model (iCHO2291) was used as a framework. Enzyme kinetic data was incorporated to define enzyme capacity constraints. Flux balance analysis was modified to include these constraints (ecFBA). The model was tested under glucose excess conditions to assess overflow metabolism. A case study on lactate metabolism was conducted to validate the approach. Clone- and media-specific variations were analyzed to demonstrate model applicability. The model was evaluated for its ability to predict mitochondrial redox usage. The ecFBA framework was used to reduce flux variability while maintaining biological relevance.

The ecFBA framework significantly reduced flux variability in the model predictions. The model captured overflow metabolism under glucose excess conditions. Oxidative phosphorylation limitations were identified as enzyme capacity constraints. Lactate-pyruvate cycling was shown to enhance mitochondrial redox utilization. Clone-specific differences in lactate metabolism were successfully deciphered. The model predicted metabolic pathway usages consistent with observed behaviors. Enzyme capacity constraints improved the accuracy of intracellular flux predictions. The iCHO2291 model with ecFBA demonstrated improved predictive power.

The integration of enzyme capacity constraints into genome-scale modeling improves flux predictions. The model successfully captures overflow metabolism under glucose excess conditions. Lactate-pyruvate cycling supports efficient mitochondrial redox utilization in CHO cells. The iCHO2291 model with ecFBA provides a reliable framework for bioprocess analysis. The approach can guide cell engineering and process optimization efforts. The model supports the development of digital twin models for biomanufacturing. The findings suggest that enzyme capacity constraints are essential for accurate modeling. The study demonstrates the potential of ecFBA for advanced bioprocessing applications.