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Record W1986834820 · doi:10.4155/bfs.12.85

Engineering<i>Saccharomyces cerevisiae</i>fermentative pathways for the production of isobutanol

2013· article· en· W1986834820 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiofuels · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIsobutanolBiofuelMetabolic engineeringFermentationChemistryYeastButanolPyruvate decarboxylaseBiochemistrySaccharomyces cerevisiaeEthanol fuelFood scienceBiochemical engineeringBiotechnologyAlcohol dehydrogenaseEnzymeEthanolBiologyEngineering

Abstract

fetched live from OpenAlex

Background: Finite supplies of petroleum-based fossil fuels, in addition to concerns about carbon emissions and energy security, have driven the search for alternative fuels that can be produced from renewable resources. Butanol, pentanol and their isomers have significant advantages over ethanol as a biofuel and these can be produced by fermentation. Results: We demonstrate that yeast can be engineered to produce isobutanol by fermentation of carbohydrate precursors. This was achieved by increasing flux through the valine biosynthetic pathway in addition to decreasing pyruvate decarboxylase activity and increasing the availability of NADPH. We found no initial improvement in isobutanol production by deleting BAT1, LEU4 and LEU9, genes encoding enzymes predicted to compete with isobutanol synthesis. Conclusion: Yeast has potential as a factory for the production of higher alcohol biofuels; however, substantial engineering will be required to achieve economically viable production levels.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.212
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it