Macroscopic Modeling of Bioreactors for Recombinant Protein Producing Pichia pastoris in Defined Medium

Presented at: BioProcessing 2020
C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Researcher at the Massachusetts Institute of Technology (MIT)
    Biography
      Moo Sun Hong is a researcher at the Massachusetts Institute of Technology (MIT) in the group of Prof. Richard D. Braatz. Moo Sun received a B.S. in Chemical and Biological Engineering from Seoul National University and was the top graduate in its College of Engineering. He received an M.S. in Chemical Engineering Practice from MIT and the Jefferson W. Tester Prize for enthusiasm and leadership in the Practice School. He has constructed mathematical models for multiple bioreactor configurations and does research in the model-based design and control of continuous protein crystallization and viral inactivation.

    Abstract

    Bioreactor modeling can inform understanding of cellular metabolism—including aspects of growth kinetics, productivity, media consumption, and metabolite secretion—and can be used to optimize bioreactor operations. Models for bioreactors are developed with different level of details and complexity depending on their purpose. For methylotrophic yeast Pichia pastoris, which is widely used as the microbial host for recombinant protein production, complex cellular models such as genomic-scale metabolic models have been developed to describe detailed cellular metabolism. These models, however, have too many parameters to be accurately estimated from experimental data. As such, most of the parameters are highly uncertain, even when additional assumptions and/or approximations are made such as the metabolic fluxes being at steady state. In contrast, macroscopic models can yield a reasonable description of the bioreactor while using simpler mathematical formulations with low computational costs. These types of models can also facilitate real-time applications such as adaptive parameter estimation and model predictive control.

    This presentation describes an extensive macroscopic bioreactor model for substrates, biomass, total protein, other medium components, and off-gas components. Species and elemental balances are introduced to describe uptake and evolution rates for medium components and off-gas components. Additionally, a pH model is constructed using an overall charge balance, acid/base equilibria, and activity coefficients to describe production of recombinant protein and precipitation of medium components. The extent of run-to-run variability is modeled by distributions of a subset of the model parameters, which are estimated using the maximum likelihood method. The uncertainty description in this macroscopic bioreactor model identifies the model parameters that have large variability and provides guidance as to which aspects of cellular metabolism should be the focus of additional experimental studies. The model for medium components with pH and precipitation can be used for improving the chemically defined medium by minimizing the amount of components needed while meeting cellular requirements.

    Learning Objectives:

    1. Discuss current trends in biopharmaceutical manufacturing operations and control

    2. Describe a mathematical model that incorporates solution chemistry to more accurately predict bioreactor data


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