Modeling of cell culture damage and recovery leads to increased antibody and biomass productivity in CHO cell cultures
Bibliographic record
Abstract
The development of an efficient and productive cell-culture process requires a deep understanding of intracellular mechanisms and extracellular conditions for optimal product synthesis. Mathematical modeling provides an effective strategy to predict, control, and optimize cell performance under a range of culture conditions. In this study, a mathematical model is proposed for the investigation of cell damage of a Chinese hamster ovary cell culture secreting recombinant anti-RhD monoclonal antibody (mAb). Irreversible cell damage was found to be correlated with a reduction in pH. This irreversible damage to cellular function is described mathematically by a Tessier-based model, in which the actively growing fraction of cells is dependent on an intracellular metabolic product acting as a growth inhibitor. To further verify the model, an offline model-based optimization of mAb production in the cell culture was carried out, with the goal of minimizing cell damage and thereby enhancing productivity through intermittent refreshment of the culture medium. An experimental implementation of this model-based strategy resulted in a doubling of the yield as compared to the batch operation and the resulting biomass and productivity profiles agreed with the model predictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".