1 research outputs found
A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
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