Real-time VOC profiling for controlling the Crabtree effect in Saccharomyces cerevisiae
Bibliographic record
Abstract
The Crabtree effect enables ethanol production in aerobic environments with high glucose concentrations. This metabolic shift produces a characteristic VOCs profile. Its mechanism is not fully understood for different reasons, including differences in growing conditions, low temporal resolution monitoring, and loss of molecular information due to saturation or high selectivity. Using a portable mass spectrometer (MoBiMS) connected directly to a bioreactor headspace, we developed a method for continuous VOC monitoring that overcomes limitations of traditional techniques regarding specificity, saturation, and temporal resolution. This approach improves resolution from hours to seconds. We used these VOC profiles to train a model to classify metabolic states as Crabtree-positive or Crabtree-negative in seconds. Furthermore, signal processing of the kinetic data reveals stimulus timing and periodicity. Our findings demonstrate the viability of in situ online VOC monitoring in bioprocesses, enabling detection of Crabtree effect induction and analysis of metabolic responses to glucose addition stimuli.
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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".