Dynamic model of Chinese hamster ovary cell metabolism in fed-batch culture

In this work, the design and execution of a mechanistic metabolic model is presented that is capable of simulating extracellular metabolite concentration profiles, particularly cell density and antibody titer, throughout the course of a recombinant protein producing CHO fed-batch culture. In Chapter 2, formulation of a reaction network is described wherein a genome scale metabolic reaction network is systematically reduced in size through the use of graph theory and elementary flux modes methodologies, resulting in a smaller but biochemically comprehensive network that can be used for dynamic kinetic modeling. In Chapter 3, a dynamic model structure is described that builds upon a dynamic flux balance analysis framework, whereby 12 intracellular cytosolic reactions are defined with convenience kinetic rate expressions and the remaining 22 intracellular and exchange reactions are calculated from mass balances around the assumed pseudo-steady state intracellular metabolites. The unique aspects of the model formulation that are vital to achieving accurate dynamic predictions include: (1) defining intracellular cytosolic reactions with kinetic rate expressions that are based on the associated extracellular metabolite concentrations, and (2) defining a redox variable to accommodate the effect of NADH on reaction rate kinetics. Application of the model is the demonstrated by predicting the effect of process variable changes (e.g. temperature, seed density, nutrient concentrations) on the resulting metabolic dynamics of a CHO fed-batch culture. In Chapter 4, further application of the model is demonstrated by simulating the metabolic responses of genetic modulations (e.g. up- or down-regulation), independently, but more importantly, in conjunction with process variable changes. Optimal operating conditions for both process and genetic variables are determined for producing improved quantity and quality of recombinant protein. Overall, the dynamic metabolic model serves as a powerful tool to aid laboratory experiments, providing savings in time, money, and resources, as well as an improved understanding of the biochemical mechanisms driving the processes.