CHO Reference (Chinese Hamster Ovary)

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.

Genome-scale metabolic models

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.

Cell culture media training datasets

CHO-GS(-/-) media-component → critical quality attributes (Gangwar et al. 2024)

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.

Further reading

• CHO-focused metabolic-modeling methodology: Papers.md ref #59 (Antonakoudis & Richelle 2026, data-driven GEM reduction for bioprocess modeling) — a CHO case study whose reduction approach is directly applicable to cell-ag digital twins.
• Constraint-based modeling tools: Software.md / Metabolic Modeling & Strain Design.
• Cell-ag livestock GEMs that adapt CHO process methodology: Cow, Pig, Chicken, Fish.
• Bioprocess context: ResearchAreas/Bioprocess.md, ResearchAreas/MetabolicModeling.md.
• Human reference reconstructions: HumanReference.