Metabolic flux‐based optimisation of recombinant human interleukin‐3 expression by <i>Streptomyces lividans 66</i>
Bibliographic record
Abstract
Abstract A metabolic reaction network including carbohydrate and amino acid metabolism in both anabolic and catabolic reactions was developed for Streptomyces lividans. Based on observed nutrient consumption, the fermentation process comprised two stages: amino acid uptake in the first phase followed by ammonia uptake as the nitrogen source in the second phase. Linear programming in combination with eight measured input and output fluxes was employed to determine the flux distribution through the major metabolic pathways in both phases of the batch fermentations. Two different objective functions were defined and examined: optimising biomass production by maximising the specific growth rate and optimising a balanced redox metabolism by minimising excess NAD(P)H production. The flux distributions obtained for the two objectives were very similar. The model predicted specific growth rate agreed well (8–10% difference) with observations. A uniform distribution was assumed for each biomass component and the results showed that, using either objective the biomass composition does not have a significant effect on the internal distribution of fluxes.
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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 | 0.000 |
| 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".