Prediction of Weak Acid Toxicity in <i>Saccharomyces cerevisiae</i> Using Genome-Scale Metabolic Models
Bibliographic record
Abstract
The use of lignocellulosic biomass is critical for the economic production of transportation fuels and chemicals in renewable bioprocesses. While biomass is an abundant resource, necessary pretreatment to yield fermentable monosaccharides produces toxic compounds that dramatically affect fermentation performance. Weak acids such as acetic acid play an important role in the toxicity of lignocellulosic hydrolysate to Saccharomyces cerevisiae , a commonly used industrial organism. In order to explore the ramifications of weak acid inhibition on cellular metabolism, we adapted a genome-scale metabolic model of S. cerevisiae to describe toxicity of acetic acid by a decoupling mechanism. We evaluated the performance of the model in predicting growth rates and ethanol production characteristics under aerobic and anaerobic cultivations. We found that the model was able to capture the decreased growth during aerobic cultivations in the presence of acetic acid, but was unable to capture the increase in ethanol yield observed. The model was able to predict anaerobic growth rates and ethanol yields; however, at conditions of higher toxicity levels, discrepancies arose. We expect that a model such as this may find application in the optimization of lignocellulose-based bioprocesses in which there exists a critical economic trade-off between neutralization costs and product yields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".