Model Based Design of a Saccharomyces cerevisiae Platform Strain with Improved Tyrosine Production Capabilities
Bibliographic record
Abstract
Large-scale production of plant secondary metabolites is of interest because of their application in production of many valuable products. Recent advances in the area of DNA recombinant technology has made it possible to produce these valuable compounds using microbial routes. The objective of this work was, to design a platform strain of Saccharomyces cerevisiae with improved intracellular tyrosine pools using computational modeling. This engineered yeast could be used as a host for producing important plant secondary metabolites on an industrial-scale. In this study, a combination of steady-state and dynamic modeling methods were used for strain design. Initial strain design was performed using steady-state modeling, and the predictions from steady-state modeling were prioritized for experimental validation using dynamic modeling. The final strategy proposed included deletion of PDC1, ZWF1, ARO10; over-expression of ALD6, and alleviation of tyrosine feedback resistance in shikimate pathway. Initial experiments for validation of this strategy showed promising results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".