Elucidating the effects of postinduction glutamine feeding on the growth and productivity of CHO cells
Bibliographic record
Abstract
Inducible mammalian expression systems are increasingly being used for the production of valuable therapeutics. In such system, maximizing the product yield is achieved by carefully balancing the biomass concentration during the production phase and the specific productivity of the cells. These two factors are largely determined by the availability of nutrients and/or the presence of toxic waste metabolites in the culture environment. Glutamine is one of the most important components of cell culture medium, since this substrate is an important building block and source of energy for biomass and recombinant protein production. Its metabolism, however, ultimately leads to the formation of ammonia, a well known inhibitor of cellular growth and productivity. In this work, we show that nutrient feeding post-induction can greatly enhance the product yield by alleviating early limitations encountered in batch. Moreover, varying the amount of glutamine in the feed yielded two distinct culture behaviors post-induction; whereas excess glutamine allowed to reach greater cell concentrations, glutamine-limited fed-batch led to increased cell specific productivity. These two conditions also showed distinctive lactate metabolism. To further assess the physiological impact of glutamine levels on the cells, a comparative (13) C-metabolic flux analysis was conducted and a number of key intracellular fluxes were found to be affected by the amount of glutamine present in the feed during the production phase. Such information may provide useful clues for the identification of physiological markers of cell growth and productivity that could further guide the optimization of inducible expression systems.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".