CHO cell engineering directed by genomescale modelling

Biopharmaceuticals (pharmaceutical products consisting of (glyco)proteins and/or nucleic acids) have revolutionised the treatment of many debilitating and life threating diseases, thanks in part to their high specificity and reduced toxicity compared to traditional small-molecule therapeutics. As a result, biopharmaceuticals boast above market growth, stimulating increased focus from global leaders within the pharmaceutical industry. This has amplified research into strategies for the intensification and improvement of manufacturing processes, aiming to reduce cost while increasing efficiency and product quality.
Biopharmaceutical products are predominantly manufactured by transfecting recombinant DNA into a host cell line, whereby the product is expressed, harvested, and purified. Chinese Hamster Ovary (CHO) cells are the most widely used mammalian host for such expression, accounting for the production of 80% of all monoclonal antibodies in the market. This is largely due to their long-term study, human compatible glycoforms, adaptability to bioreactors and ease of genetic manipulation. Despite their long-term and widespread use however, there remains significant scope to improve CHO expression systems through directed cell line engineering, allowing for the intensification and improvement of manufacturing processes.
Genome-scale modelling (GeM) is a computational methodology representing a structured database of all known metabolic processes that take place within a cell. By integrating the metabolites, enzymes and genes involved within each metabolic process, these models can be applied to compute intracellular metabolic fluxes, gene expression regulation and protein secretion. When coupled with optimization algorithms, GeMs allows for the identification of potential genetic engineering strategies. For instance, by identifying non-essential metabolic pathways, candidate genes may be identified for knockout/knockdown, potentially freeing up cellular resources and reducing host cell proteins, improving product quality and productivity. Substantial effort has already been invested into building a CHO GeM. Significantly, a generic CHO GeM model, iCHO1766, containing most known CHO genes, enzymes and metabolites was published in 2016. This model was recently expanded by Gutierrez et al. to include the secretory pathway(iCHO2048), enabling the computation of energetic costs and machinery demands of each secreted protein.
These modelling efforts clearly pave the way for research into directed CHO cell engineering. Recently, Kol et al. utilised this iCHO2048 GeM to generate knockout clones for host cell proteins, reporting higher productivity and improved growth characteristics in specific clones, while reducing pressure in downstream processing. Outside of this work however, there remains few examples utilising these models to directed CHO cell engineering strategies, making it an attractive area for investigation.
This PhD project therefore aims to explore this research gap. Firstly, by expanding the iCHO2048 GeM, for instance by including post translational modification and protein degradation pathways, to improve biological relevance and allow superior modelling capabilities. Critically, for effective modelling, and therefore effective directed cell engineering, it is vital to constrain models with experimental data. This model shall therefore be constrained and reduced using metabolic, progress to apply optimisation algorithms to models to identify targets for genetic engineering, primarily aiming to optimise cell specific productivity, before selected targets are validated in the wet lab.