A Bayesian
Approach to Run-to-Run Optimization of
Animal Cell Bioreactors Using Probabilistic Tendency Models
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Abstract
Increasing
demand for recombinant proteins (including monoclonal
antibodies) where time to market is critical could benefit from the
use of model-based optimization of cell viability and productivity.
Owing to the complexity of metabolic regulation, unstructured models
of animal cell cultures typically have built-in errors (structural
and parametric uncertainty) which give rise to the need for obtaining
relevant data through experimental design in modeling for optimization.
A Bayesian optimization strategy which integrates tendency models
with iterative policy learning is proposed. Parameter distributions
in a probabilistic model of bioreactor performance are re-estimated
using data from experiments designed for maximizing information content
and productivity. Results obtained highlight that experimental design
for run-to-run optimization using a probabilistic tendency model is
effective to maximize biomass growth even though significant model
uncertainty is present. A hybrid cybernetic model of a myeloma cell
culture coconsuming glucose and glutamine is used to simulate data
to demonstrate the efficacy of the proposed approach