Vaziri, B. | Search bioRxiv

bioRxiv2020

Systematically gap-filling the genome-scale model of CHO cells

ObjectiveChinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells. ResultsWe expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased. ConclusionsThe present CHO model is an important step towards more complete metabolic models of CHO cells.

systems biology↗

bioRxiv2019

A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production

Chinese hamster ovary (CHO) cells are the main workhorse in the biopharmaceutical industry for the production of recombinant proteins, such as monoclonal antibodies. To date, a variety of metabolic engineering approaches have been used to improve the productivity of CHO cells. While genetic manipulations are potentially laborious in mammalian cells, rational design of CHO cell culture medium or efficient fed-batch strategies are more popular approaches for bioprocess optimization. In this study, a genome-scale metabolic network model of CHO cells was used to design feeding strategies for CHO cells to improve monoclonal antibody (mAb) production. A number of metabolites, including threonine and arachidonate, were suggested by the model to be added into cell culture medium. The designed composition has been experimentally validated, and then optimized, using design of experiment methods. About a two-fold increase in the total mAb expression has been observed using this strategy. Our approach can be used in similar bioprocess optimization problems, in order to suggest new ways of increasing production in different cell factories.

bioengineering↗