The Chinese Hamster Ovary (CHO) cell line is the dominant mammalian host for recombinant-protein biomanufacturing. CHO is not a cellular agriculture species, but it is the closest mature analogue for cell-ag process modeling: its genome-scale metabolic models are the most-developed mammalian-cell GEM ecosystem available, and its biomass parameterization, perfusion-process methodology, and model-reduction techniques translate directly to the cultivated-meat GEMs (bovine, porcine, avian, salmonid) catalogued on the per-species pages of this directory. This page collects the CHO reference reconstructions as a biopharma-adjacent substrate.
iCHO1766 / iCHO2048 / CHOmpact — Chinese Hamster Ovary (biopharma-adjacent reference)
The CHO cell line is the mammalian biopharma workhorse, and its GEM family is the most-developed mammalian GEM ecosystem available — Hefzi et al.’s iCHO1766 (2016, Cell Systems) is the consensus reconstruction; iCHO2048 (2018) extends the secretory pathway; CHOmpact (2024) and follow-on Bayesian-flux-estimation pipelines (2025) produce reduced models for digital-twin work. CHO is not itself a cellular agriculture species, but its biomass parameterization, perfusion-process methodology, and reduction techniques translate directly to cell-ag GEMs (bovine, porcine, avian) currently under construction.
Reference: Papers.md #85 (Hefzi et al. 2016, Cell Systems).
Curation source: This entry is long-standing CAAIL curation, migrated from the prior flat Datasets.md. CHO is a biopharma-adjacent reference rather than a cultivated-meat species, so it is not drawn from the Todhunter et al. 2024 supplemental.
A supervised-learning training table covering CHO-GS(-/-) cell culture in chemically defined media of varying metal-ion composition and the resulting critical quality attributes (CQAs). Generated by Gangwar, Balraj, & Rathore (2024, Applied Microbiology and Biotechnology 108(1), 308) to train an end-to-end explainable-AI framework for media-component selection and CQA prediction; the tree-ensemble feature-attribution analysis (XGBoost / Gradient Boosting / Decision Trees / Random Forest / CatBoost with SHAP) is what places Papers.md #170 in the matrix’s Ensemble Learning × Media Optimization cell. The training table is distributed only as supplementary material with the paper — no public repository accession.
cited by19
These CHO datasets pair antibody-producing cell-culture runs with omics readouts — multi-omic time courses, spent-media metabolomics, and clone-level metabolomic dynamics. They are the measurement layer that constrains and validates the mammalian-cell GEMs above, and the same profiling logic transfers to the cell-ag livestock GEMs (bovine, porcine, avian) under construction on the per-species pages: which nutrients limit growth, how metabolism reprograms across a run, and which metabolic signatures separate a productive line from an unproductive one.
CHO multi-omic culture-pH profiling dataset (Lee et al. 2021)
A multi-omics profiling of a CHO cell-culture system that resolves how culture pH shifts cell growth, antibody titer, and product quality (Lee, Kok, Lakshmanan, et al. 2021, Biotechnology and Bioengineering). The paired transcriptomic, proteomic, and metabolomic layers make this a parameterization and validation substrate for mammalian-cell GEMs and for process models that couple pH set-points to CQAs — the same growth-versus-quality trade-off cell-ag bioprocess models have to capture. Published: 10.1002/bit.27899.
cited by39
CHO antibody-producer multi-omic dataset (Gopalakrishnan et al. 2024)
A multi-omic characterization of antibody-producing CHO cell lines that maps metabolic reprogramming and pinpoints nutrient-uptake bottlenecks across production (Gopalakrishnan, Johnson, Valderrama-Gomez, et al. 2024, Metabolic Engineering, from the Lewis lab). The nutrient-limitation and flux-reprogramming signals are directly usable for constraining GEM flux states and for designing feeds — methodology that carries over to media and feed design for cultivated-meat cell lines. Published: 10.1016/j.ymben.2024.07.009.
cited by13
A spent-media metabolomics dataset built to identify cell-line-independent indicators of growth inhibition in CHO bioprocesses (Alden, Raju, McElearney, et al. 2020, Metabolites). Because the growth-inhibition markers are framed to generalize across cell lines rather than to one clone, the dataset is a template for the spent-media metabolite profiling that cell-ag media optimization relies on to diagnose why a formulation stalls. Published: 10.3390/metabo10050199.
cited by32
A metabolomic-dynamics dataset supporting machine-learning selection of productive CHO cell lines during biopharma process development (Barberi, Benedetti, Diaz-Fernandez, et al. 2025, AIChE Journal). The time-resolved metabolomic trajectories, used to classify high- from low-producing clones, are a methodology template for metabolomics-guided line selection in cultivated-meat cell-line development, where picking a stable high-performing line early is a core bottleneck. Published: 10.1002/aic.18602.
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Bioprocess characterization datasets
These datasets characterize CHO bioprocess behaviour at the resolution constraint-based and kinetic models need — the process-mode and flux measurements that parameterize genome-scale models, reduce them for digital-twin work, and tune perfusion / fed-batch process models. Both are directly relevant to the cell-ag GEMs catalogued on the per-species pages.
Pseudo-perfusion CHO characterization dataset (Malinov et al. 2026)
A validated pseudo-perfusion characterization of CHO-K1 cells producing the VRC01 monoclonal antibody (Malinov et al. 2026, Biotechnology and Bioengineering) — a reliable data-generation platform for modelling and guiding continuous perfusion biomanufacturing. Supports GEM parameterization and reduction and digital-twin tuning for the cell-ag process models under construction; the underlying data are available from the authors on request. Published: 10.1002/bit.70190 (preprint 10.1101/2025.11.27.691016).
cited by2
A CHO-VRC01 flux dataset spanning fed-batch and perfusion process modes across bioreactor pH, media supplementation, and feeding strategies (Venkatarama Reddy et al. 2026, bioRxiv), paired with a dynamic metabolic-flux-analysis model that regresses kinetic parameters across amino-acid metabolism and cell growth. Sits alongside the pseudo-perfusion dataset as GEM-parameterization substrate for cell-ag digital twins. Preprint: 10.64898/2026.01.11.698917.
cited by1
Raman soft-sensor cell-culture dataset (Tanemura et al. 2023)
A cell-culture dataset pairing in-line Raman spectra with offline culture-profile measurements, used to train machine-learning soft sensors that predict the culture state from spectra (Tanemura, Kitamura, Yamada, et al. 2023, Scientific Reports). Raman-plus-ML soft sensors are the real-time monitoring layer that continuous and perfusion cell-ag bioprocesses need, so this dataset is a direct template for building spectroscopic state estimators for cultivated-meat reactors. Published: 10.1038/s41598-023-49257-0.
cited by24
Further reading