Error propagation from prime variables into specific rates and metabolic fluxes for mammalian cells in perfusion culture
Bibliographic record
Abstract
Error propagation from prime variables into specific rates and metabolic fluxes was quantified for high-concentration CHO cell perfusion cultivation. Prime variable errors were first determined from repeated measurements and ranged from 4.8 to 12.2%. Errors in nutrient uptake and metabolite/product formation rates for 5-15% error in prime variables ranged from 8-22%. The specific growth rate, however, was characterized by higher uncertainty as 15% errors in the bioreactor and harvest cell concentration resulted in 37.8% error. Metabolic fluxes were estimated for 12 experimental conditions, each of 10 day duration, during 120-day perfusion cultivation and were used to determine error propagation from specific rates into metabolic fluxes. Errors of the greater metabolic fluxes (those related to glycolysis, lactate production, TCA cycle and oxidative phosphorylation) were similar in magnitude to those of the related greater specific rates (glucose, lactate, oxygen and CO(2) rates) and were insensitive to errors of the lesser specific rates (amino acid catabolism and biosynthesis rates). Errors of the lesser metabolic fluxes (those related to amino acid metabolism), however, were extremely sensitive to errors of the greater specific rates to the extent that they were no longer representative of cellular metabolism and were much less affected by errors in the lesser specific rates. We show that the relationship between specific rate and metabolic flux error could be accurately described by normalized sensitivity coefficients, which were readily calculated once metabolic fluxes were estimated. Their ease of calculation, along with their ability to accurately describe the specific rate-metabolic flux error relationship, makes them a necessary component of metabolic flux analysis.
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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 | 0.007 | 0.024 |
| 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 | 0.001 | 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".