A semi‐empirical mathematical model to specify the <scp>pH</scp> of bicarbonate‐buffered cell culture medium formulations
Bibliographic record
Abstract
Abstract The carbon dioxide/bicarbonate system is routinely used as a buffer in mammalian cell culture medium. However, due to the continuous degassing of carbon dioxide and its dependence on temperature, it is difficult to achieve the target pH during preparation at ambient temperature and without control of dissolved carbon dioxide, and even more so at cell culture operating conditions. These problems were amplified when preparing multiple (e.g., 20) customized preparations of an in‐sourced proprietary medium during research and process development of mammalian cell culture systems. Thus, a mathematical model was created to specify the amount of acid or base to add during preparation so as to achieve the target pH of each medium at process conditions without having to do titration. The relationship between gaseous carbon dioxide and the dissolved carbon dioxide in the proprietary medium containing unknown species was specified using a modified Henry's law equation (as done in prior work). Further, to allow medium preparation without doing titration, the acid/base properties of the proprietary medium were fitted by a parameter related to its Net Medium Acids during specification of model parameters. A subset of the solutions was prepared and tested in bioreactors with controlled CO 2 flow to validate the model. Then, the model was used to assess the equivalence of the pHs of the customized medium formulations despite variations in pCO 2 that occurred during incubation and sampling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".