293SF Metabolic Flux Analysis during Cell Growth and Infection with an Adenoviral Vector
Bibliographic record
Abstract
Metabolic flux quantification of cell culture is becoming a crucial means to improve cell growth as well as protein and vector productions. The technique allows rapid determination of cell culture status, thus providing a tool for further feeding improvements. Herein, we report on key results of a metabolic investigation using 293 cells adapted to suspension and serum-free medium (293SF) during growth and infection with an adenoviral vector encoding the green fluorescence protein (GFP). The model developed contains 35 fluxes, which include the main fluxes of glycolysis, glutaminolysis, and amino acids pathways. It requires specific consumption and production rate measurements of amino acids, glucose, lactate, NH(3), and O(2), as well as DNA and total proteins biosynthesis rate measurements. Also, it was found that extracellular protein concentration measurement is important for flux calculation accuracy. With this model, we are able to describe the 293SF cell metabolism, grown under different culture conditions in a 3-L controlled bioreactor for batch and fed-batch with low glucose. The metabolism is also investigated during infection under two different feeding strategies: a fed-batch starting at the end of the growth phase and extending during infection without medium change and a fed-batch after infection following medium renewal. Differences in metabolism are observed between growth and infection, as well as between the different feeding strategies, thus providing a better understanding of the general metabolism.
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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) | 0.001 | 0.001 |
| 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) | 0.000 | 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".