Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells.

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells.

A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures.

The control of nutrient availability is critical to large-scale manufacturing of biotherapeutics. However, the quantification of proteinogenic amino acids is time-consuming and thus is difficult to implement for real-time in situ bioprocess control. Genome-scale metabolic models describe the metabolic conversion from media nutrients to proliferation and recombinant protein production, and therefore are a promising platform for in silico monitoring and prediction of amino acid concentrations.

Improvement of growth rates through nucleoside media supplementation of CHO clones.

Chinese Hamster Ovary (CHO) cells are used for the production of therapeutic proteins. This work examines improving passaging growth rate of two CHO clones. Growth rates were significantly improved for both clones with supplementation of the nucleosides cytidine, hypoxanthine, uridine, and thymidine to the culturing media at the optimal concentration of 100 µM of each nucleoside.