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Chinese hamster ovary (CHO) cells are characterized by a low glucose catabolic efficiency, resulting in undesirable lactate production. Here, it is hypothesized that such low efficiency is determined by the transport of pyruvate into the mitochondria. The mitochondrial pyruvate carrier (MPC), responsible for introducing pyruvate into the mitochondria, is formed by two subunits, MPC1 and MPC2. Stable CHO cell lines, overexpressing the genes of both subunits, were constructed to facilitate the entry of pyruvate into the mitochondria and its incorporation into oxidative pathways. Significant overexpression of both genes, compared to the basal level of the control cells, was verified, and subcellular localization of both subunits in the mitochondria was confirmed. Kinetic evaluation of the best MPC overexpressing CHO cells showed a reduction of up to 50% in the overall yield of lactate production with respect to the control. An increase in specific growth rate and maximum viable cell concentration, as well as an increase of up to 40% on the maximum concentration of two recombinant model proteins transiently expressed (alkaline phosphatase or a monoclonal antibody), was also observed. Hybrid cybernetic modeling, that considered 89 reactions, 25 extracellular metabolites, and a network of 62 intracellular metabolites, explained that the best MPC overexpression case resulted in an increased metabolic flux across the mitochondrial membrane, activated a more balanced growth, and reduced the Warburg effect without compromising glucose consumption rate and maximum cell concentration. Overall, this study showed that transport of pyruvate into the mitochondria limits the efficiency of glucose oxidation, which can be overcome by a cell engineering approach.

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Chinese hamster ovary (CHO) cell culture has a major importance on the production of biopharmaceuticals, including recombinant therapeutic proteins such as monoclonal antibodies (MAb). Mathematical modeling of biological systems can successfully assess metabolism complexity while providing logical and systematic methods for relevant genetic target and culture parameter identification toward cell growth and productivity improvements. Most modeling approaches on CHO cells have been performed under stationary constraints, and only a few dynamic models have been presented on simplified reaction sets, due to substantial overparameterization problems. The hybrid cybernetic modeling (HCM) approach has been recently used to describe the dynamic behavior by incorporating regulation between different metabolic states by elementary mode participation control, with sets of equations evaluated by objective functions. However, as metabolic networks evaluated are constructed toward a genomic scale, and cell compartmentalization is considered, identification of the active set becomes more difficult as EM number exponentially grows. Thus, the development of robust approaches for EM active set selection and analysis with smaller computational requirements is required to impulse the use of cybernetic modeling on larger up to genome-scale networks. In this report, a novel elementary mode selection strategy, based on a polar representation of the convex solution space is presented and coupled to a cybernetic approach to model the dynamic physiologic and metabolic behavior of CHO-S cell cultures. The proposed Polar Space Yield Analysis (PSYA) was compared to other reported elementary mode selection approaches derived from Common Metabolic Objective Analysis (CMOA) used in Flux Balance Analysis (FBA), Yield Space Analysis (YSA), and Lumped Yield Space Analysis (LYSA). For this purpose, exponential growth phase dynamic metabolic models were calculated using kinetic rate equations based on previously modeled growth parameters. Finally, complete culture dynamic metabolic flux models were constructed using the HCM approach with selected elementary mode sets. The yield space elementary mode-and Martínez et al.Dynamic Modeling of CHO Cells the polar space elementary mode-hybrid cybernetic models presented the best fits and performances. Also, a flux reaction perturbation prediction approach based on the polar yield solution space resulted useful for metabolic network flux distribution capability analysis and identification of potential genetic modifications targets.

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