Genome-Scale Dynamic Flux Balance Analysis of Aerobic Batch Cultures of Recombinant Pichia Pastoris Strains

Pichia pastoris is a methylotrophic yeast widely-used for the production of recombinant proteins. It has a post-translational machinery similar to higher eukaryotes, grows up to high cell densities and has a low secretion rate of native proteins, which makes downstream processing easier.

Since Pichia pastoris genome sequencing, three genome-scale models have been constructed to understand its metabolism under different conditions and to improve recombinant protein production. However, the application of these models to obtain metabolic insights of dynamic process conditions, which are key to recombinant protein production at industrial scale, has not been addressed yet.

Here, we applied Dynamic Flux Balance Analysis (DFBA) with a multi-objective optimization approach to develop a robust dynamic genome-scale metabolic model of Pichia pastoris to simulate Batch and Fed-batch cultures. The model operates under a pseudo-steady state assumption and it is solved iteratively. The model is able to: (i) fit extracellular metabolite data; (ii) predict metabolic flux distributions throughout the process; and (iii) assess metabolic engineering strategies to increase the volumetric productivity of the recombinant protein of interest.

The model was calibrated with extracellular metabolite data from glucose-limited batch cultures of different Pichia pastoris recombinant strains. Each strain harbored a different number of copies of the sweet-tasting protein thaumatin, which has potential to reduce the sugar content of foods. The model showed significant sub-optimal growth in all strains and reproduced the extracellular product profiles and the observed decrease in growth rate as gene copy number increased.

In addition, the model was used to simulate single knockout candidates to improve recombinant thaumatin production using the Minimization of Metabolic Adjustment algorithm (MOMA) as objective function. Several candidates that increased the protein volumetric productivity [mg/L·h] at user-defined initial substrate concentrations were selected for future implementation of the mutations.

Although model predictions may be improved by model curation, integration of omic data and incorporation of post translational modifications, new testable hypotheses to improve recombinant thaumatin production have emerged from this analysis.