dc.creator | Luna, Martín Francisco | |
dc.creator | Martinez, Ernesto Carlos | |
dc.date.accessioned | 2017-08-15T14:48:32Z | |
dc.date.accessioned | 2018-11-06T13:17:51Z | |
dc.date.available | 2017-08-15T14:48:32Z | |
dc.date.available | 2018-11-06T13:17:51Z | |
dc.date.created | 2017-08-15T14:48:32Z | |
dc.date.issued | 2014-08 | |
dc.identifier | Luna, Martín Francisco; Martinez, Ernesto Carlos; A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models; American Chemical Society; Industrial & Engineering Chemical Research; 53; 44; 8-2014; 17252-17266 | |
dc.identifier | 0888-5885 | |
dc.identifier | http://hdl.handle.net/11336/22438 | |
dc.identifier | CONICET Digital | |
dc.identifier | CONICET | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1874004 | |
dc.description.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. | |
dc.language | eng | |
dc.publisher | American Chemical Society | |
dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/ie500453e | |
dc.relation | info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie500453e | |
dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.subject | RUN-TO-RUN OPTIMIZATION | |
dc.subject | BIOPROCESS | |
dc.subject | TENDENCY MODELS | |
dc.title | A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models | |
dc.type | Artículos de revistas | |
dc.type | Artículos de revistas | |
dc.type | Artículos de revistas | |