Enzyme capacity-based genome scale modelling of CHO cells
Open accessPublished version of a preprint
Abstract
Chinese hamster ovary (CHO) cells are most prevalently used for producing recombinant therapeutics in biomanufacturing. Recently, more rational and systems approaches have been increasingly exploited to identify key metabolic bottlenecks and engineering targets for cell line engineering and process development based on the CHO genome-scale metabolic model which mechanistically characterizes cell culture behaviours. However, it is still challenging to quantify plausible intracellular fluxes and discern metabolic pathway usages considering various clonal traits and bioprocessing conditions. Thus, we newly incorporated enzyme kinetic information into the updated CHO genome-scale model (iCHO2291) and added enzyme capacity constraints within the flux balance analysis framework (ecFBA) to significantly reduce the flux variability in biologically meaningful manner, as such improving the accuracy of intracellular flux prediction. Interestingly, ecFBA could capture the overflow metabolism under the glucose excess condition where the usage of oxidative phosphorylation is limited by the enzyme capacity. In addition, its applicability was successfully demonstrated via a case study where the clone- and media-specific lactate metabolism was deciphered, suggesting that the lactate-pyruvate cycling could be beneficial for CHO cells to efficiently utilize the mitochondrial redox capacity. In summary, iCHO2296 with ecFBA can be used to confidently elucidate cell cultures and effectively identify key engineering targets, thus guiding bioprocess optimization and cell engineering efforts as a part of digital twin model for advanced biomanufacturing in future.
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The DLKcat-based pipeline improves the number of reactions with enzymatic constraints in comparison with original-ecGEMs that have previously been constructed.
“it even improves the number of reactions with enzymatic constraints in comparison with original-ecGEMs that have previously been constr ucted 2,8-12,50 .”
From Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction (2022) · cites this paper as [11]
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References 56 · Cited by 75
- Authors
- Yeo, H. C., Hong, J., Lakshmanan, M., & Lee, D.-Y.
- Affiliations
- Agency for Science, Technology and Research (SG)
- Corresponding author
- Lakshmanan, Meiyappan
- Lee, Dong-Yup
- Venue
- Metabolic Engineering
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 60
- Pages
- 138-147
- ISSN
- 1096-7176 (ISSN-L)
- OpenAlex
- W3019926668
- Cited by
- 75
- References
- 56
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 15 · 2023: 12 · 2022: 10 · 2021: 12 · 2020: 5 · 2019: 1
- FWCI
- 3.17
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Metabolic Engineering (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Funders
- Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore
- National Research Foundation of Korea
- MSIT (2020R1A2C2007192)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Viral Infectious Diseases and Gene Expression in Insects
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- CHO cells, genome-scale metabolic models, enzyme kinetics, flux balance analysis, oxidative phosphorylation, lactate metabolism, cell line engineering, bioprocess optimization, digital twin, recombinant protein expression
- MeSH terms
- Animals, Cricetulus, Enzymes, Glucose, Cricetinae, Kinetics, Mitochondria, Models, Genetic, Oxidation-Reduction, CHO Cells, Pyruvic Acid, Lactic Acid, Metabolic Networks and Pathways, Genome Size, Metabolic Engineering
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