When Do Two‐Stage Processes Outperform One‐Stage Processes?
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Bibliographic record
Abstract
Apart from product yield and titer, volumetric productivity is a key performance indicator for many biotechnological processes. Due to the inherent trade-off between the production of biomass as catalyst and of the actual target product, yield and volumetric productivity cannot be optimized simultaneously. Therefore, in combination with genetic techniques for dynamic regulation of metabolic fluxes, two-stage fermentations (TSFs) with separated growth and production phase have recently gained much interest because of their potential to improve the productivity of bioprocesses while still allowing high product yields. However, despite some successful case studies, so far it has not been discussed and analyzed systematically whether or under which conditions a TSF guarantees superior productivity compared to one-stage fermentation (OSF). In this study, we use mathematical models to demonstrate that the volumetric productivity of a TSF is not automatically better than of a corresponding OSF. Our analysis reveals that the sharp decrease of the specific substrate uptake rate usually observed in (non-growth) production phases severely impacts the volumetric productivity and thus raises a big challenge for designing competitive TSF processes. We discuss possible approaches such as enforced ATP wasting to improve substrate utilization rates in the production phase by which TSF processes can become superior to OSF. We also analyze additional factors influencing the relative performance of OSF and TSF and show that OSF processes can be more appropriate if a high product yield is an economic constraint. In conclusion, a careful assessment of the trade-offs between substrate uptake rates, yields, and productivity is necessary when deciding for OSF vs. TSF processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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 it