Development and application of genome-scale models in chinese hamster ovary cell culture
File(s)
Author(s)
Morrissey, Richard James
Type
Thesis
Abstract
Chinese hamster ovary (CHO) cells are central to the biopharmaceutical industry due to their ability to perform human-like post-translational modifications, ensuring product efficacy and safety. Optimised for high yield, stability, and scalability, CHO cells are the preferred choice for producing monoclonal antibodies and other biopharmaceuticals. Understanding CHO cell metabolism is essential for bioprocess optimisation, as product quality and titre depend on the cells’ metabolic performance. Genome-scale models (GEMs), which map an organism’s metabolic capabilities, provide a framework for understanding and improving CHO cell metabolism.
Chapter 1 reviews stoichiometric modelling in CHO cell bioprocessing, emphasising the integration of omics data with GEMs to enhance predictive accuracy. Chapter 2 evaluates CHO GEMs’ reliability, introducing the iCHO2441 model for better phenotype and flux prediction. Addressing limitations, Chapter 3 presents NEXT-FBA, a hybrid stoichiometric/data-driven approach using exometabolomic data to predict biologically relevant intracellular flux constraints. By training an artificial neural network (ANN) with exometabolomic and fluxomic data, NEXT-FBA simplifies model constraints and improves prediction accuracy.
Subsequent chapters apply GEMs to tackle lactate accumulation, a key industrial challenge. Chapter 4 identifies two mechanisms for lactate build-up: disrupted NAD+/NADH balance and pyruvate excess, linked to impaired oxidative phosphorylation and high glycolytic flux. Chapter 5 validates the role of NAD+ biosynthesis in reducing lactate accumulation through nicotinamide (NAM) supplementation, which enhances NAD+ synthesis and induces lactate consumption. In contrast, nicotinic acid (NA) supplementation is less effective, impairing cell viability.
Chapter 6 explores L-carnitine (LC) supplementation as a strategy to mitigate pyruvate accumulation. While LC improves cell viability and reduces lactate under stress conditions, its effects are limited during exponential growth, highlighting metabolic constraints. These findings underscore GEMs’ utility in developing targeted bioprocess optimisation strategies.
Chapter 1 reviews stoichiometric modelling in CHO cell bioprocessing, emphasising the integration of omics data with GEMs to enhance predictive accuracy. Chapter 2 evaluates CHO GEMs’ reliability, introducing the iCHO2441 model for better phenotype and flux prediction. Addressing limitations, Chapter 3 presents NEXT-FBA, a hybrid stoichiometric/data-driven approach using exometabolomic data to predict biologically relevant intracellular flux constraints. By training an artificial neural network (ANN) with exometabolomic and fluxomic data, NEXT-FBA simplifies model constraints and improves prediction accuracy.
Subsequent chapters apply GEMs to tackle lactate accumulation, a key industrial challenge. Chapter 4 identifies two mechanisms for lactate build-up: disrupted NAD+/NADH balance and pyruvate excess, linked to impaired oxidative phosphorylation and high glycolytic flux. Chapter 5 validates the role of NAD+ biosynthesis in reducing lactate accumulation through nicotinamide (NAM) supplementation, which enhances NAD+ synthesis and induces lactate consumption. In contrast, nicotinic acid (NA) supplementation is less effective, impairing cell viability.
Chapter 6 explores L-carnitine (LC) supplementation as a strategy to mitigate pyruvate accumulation. While LC improves cell viability and reduces lactate under stress conditions, its effects are limited during exponential growth, highlighting metabolic constraints. These findings underscore GEMs’ utility in developing targeted bioprocess optimisation strategies.
Version
Open Access
Date Issued
2024-11-01
Date Awarded
01/01/2025
License URL
Advisor
Kontoravdi, Cleo
Sponsor
Biotechnology and Biological Sciences Research Council (Great Britain)
Publisher Department
Chemical Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)