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Updated: Jul 12, 2026
09:28
Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
Published on: May 18, 2020
Hybrid elementary flux analysis/nonparametric modeling: application for bioprocess control.
Ana P Teixeira1, Carlos Alves, Paula M Alves
1IBET/ITQB, Apartado 12, P-2781-901 Oeiras, Portugal. ana.teixeira@dq.fct.unl.pt <ana.teixeira@dq.fct.unl.pt>
BMC Bioinformatics
|January 31, 2007
Summary
This study introduces a novel bioreactor control method using metabolic networks for advanced bioprocess monitoring. The approach optimizes feeding strategies, improving final product concentration in cell cultures.
Area of Science:
- Biotechnology
- Systems Biology
- Metabolic Engineering
Background:
- Omics sciences provide deep biological insights but are underutilized in industrial bioprocess control.
- Current bioprocess monitoring lacks advanced integration of metabolic knowledge.
- Bioreactor control requires sophisticated methods for optimizing industrial biological systems.
Purpose of the Study:
- To develop a novel bioreactor optimal control method based on metabolic network information.
- To integrate hybrid rigorous/data-driven systems for bioprocess dynamics.
- To utilize metabolism elementary modes for enhanced bioprocess monitoring and control.
Main Methods:
- Decomposition of metabolic networks into elementary modes (EMs) for simplified reaction mechanisms.
- Formulation of dynamical hybrid systems incorporating material balance, EM stoichiometry, and kinetics.
- Data-driven identification of unknown kinetic terms for EMs.
- Online optimization of feeding strategies based on re-estimated model parameters.
Main Results:
- Quantification of fluxes through individual EMs, identifying dominant metabolic pathways.
- Analysis of recombinant Baby Hamster Kidney (BHK-21A) cell cultures producing a fusion glycoprotein.
- Observed typical metabolic responses to glucose and glutamine during cell growth and product synthesis.
- Demonstrated improved final product concentration via online optimization of feeding strategies.
Conclusions:
- A novel bioreactor optimal control method leveraging detailed metabolic information is presented.
- The method enables identification of metabolic structural changes over batch time.
- This approach enhances bioprocess control by integrating systems biology insights.
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