Engineering yeast lipids for production of designer biodiesel
Bibliographic record
Abstract
Biodiesels constitute a growing class of fuel in the world which is increasingly inclined towards more ecological and sustainable energy. Despite their advantages, biodiesels are of limited cold flow properties and of larger NOx emissions, which are mostly attributed to the chemical composition of their oil feedstocks. This study presents a novel approach to produce Genetically Engineered Biodiesel from genetically manipulated oleaginous yeast oils for improving biodiesel properties and performances. Using full-factorial central composite design, the best chemical composition of an optimal biodiesel was predicted. Then, simple and combined MFE1, PEX10 and POX2 mutants of the oleaginous yeast Yarrowia lipolytica were constructed and showed interesting lipid profiles whose biodiesel is predicted to have better cold flow properties. These mutants showed also higher lipid titers by 2-3 folds compared to the parent strain. This study provides a genetic engineering strategy for tailor design of biodiesel properties and performance.
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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 | 0.000 |
| 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 | 0.000 | 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".