(368bn) Integrating Industry Leading Datasets with Genome Scale Metabolic Models to Direct Chinese Hamster Ovary (CHO) Cell Metabolic Engineering.

I am a top-performing Computational Systems Biology PhD student at Imperial College London with extensive computational, laboratory, industrial, teaching and science communications experience. My research exists at the interface of Engineering and Biology with my interests centred on biotechnology, focusing on how in silico approaches may be used to understand and improve industrial biotechnology systems.

PhD project overview

Chinese hamster ovary (CHO) cells are the leading platform for therapeutic protein production, meaning increasing the efficiency of these cell lines is vital to meet demands and reduce costs. CHO cell genome-scale metabolic models (GeMs) possess the power to revolutionise cell line efficiency by their ability to predict whole cell metabolism in silico, allowing for model directed metabolic engineering strategies to be implemented. Despite their power, the industrial application of GeMs to improve CHO cell hosts has yet to be fully realised.

During my PhD, I developed a novel computational methodology that demonstrates how GeMs can be effectively utilised in an industrial setting to direct the design of CHO cell genetic engineering strategies. The latest CHO cell GeM (iCHO2441) is coupled with an industry leading GSK dataset, which includes time course process, metabolomic and transcriptomic data from 22 industrial cell lines with a range of performance attributes. A GSK cell line specific GeM is generated, which is used to direct cell engineering strategies following two key methods: 1. data driven analysis of predicted metabolic fluxes and 2. effect of host cell proteins on phenotype predictions.

These two methods identify a diverse range of cell engineering strategies to potentially improve GSK’s host cell line, including metabolic engineering targets in cholesterol metabolism, oxidative phosphorylation, and deletion of host cell proteins. These strategies have been experimentally validated in house at GSKs R&D facility, demonstrating significant improvements in product titres. Overall, this work presents some of the first efforts to utilise GeMs to direct mammalian cell metabolic engineering in an industrial context, utilising big datasets and a widely applicable novel modelling workflow, that is demonstrated experimentally to improve cell line phenotype.

Research Interests

My research interests are broad, being both academic and industrially focused. These interests include: Computational biology, Biotechnology, 'Omics data, Pharmaceutical manufacturing, Biopharma, Metabolic engineering, Synbio, Genome-scale modelling, Machine learning, Bioprocessing, Cell culture.