‘Omics’ technologies and systems biology for engineering<i>Saccharomyces cerevisiae</i>strains for lignocellulosic bioethanol production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To serve as the biocatalyst of choice for a viable lignocellulosic-based bioethanol industry, Saccharomyces cerevisiae will require extensive metabolic reprogramming for enhanced capabilities, including increased tolerance to fermentation inhibitors found in lignocellulosic hydrolysates, pentose fermentation pathways and potentially expression of cellulase activity while maintaining industrial productivity levels. Engineering of these complex traits will be facilitated by an in-depth understanding of the S. cerevisiae cellular system as a whole, incorporating transcript and protein expression, metabolite and lipid profiles and the genes that influence them. Such knowledge is being generated by taking an integrated systems approach using current and emerging ‘omics’ tools. These technologies have already generated understanding and novel targets for engineering of S. cerevisiae strains, and provided the data necessary for metabolic modeling in order to aid future strain development to incorporate the multitude of traits desired of a lignocellulosic biomass to bioethanol process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it