A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models

MARTIN F. LUNA; E. C. MARTÍNEZ

INDUSTRIAL & ENGINEERING CHEMICAL RESEARCH

AMER CHEMICAL SOC

Lugar: Washington; Año: 2014 vol. 53 p. 17252 - 17266

0888-5885

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.