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Plant surface lipid biosynthetic pathways and their utility for metabolic engineering of waxes and hydrocarbon biofuels

2008· article· en· W2120078999 on OpenAlexaff
Reinhard Jetter, Ljerka Kunst

Bibliographic record

VenueThe Plant Journal · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWaxMetabolic engineeringWax esterAlkaneCuticle (hair)Metabolic pathwayBiofuelBiochemical engineeringHydrocarbonBiologyChemistryGeneBiotechnologyBiochemistryOrganic chemistryEngineeringGenetics

Abstract

fetched live from OpenAlex

Due to their unique physical properties, waxes are high-value materials that are used in a variety of industrial applications. They are generated by chemical synthesis, extracted from fossil sources, or harvested from a small number of plant and animal species. As a result, the diversity of chemical structures in commercial waxes is low and so are their yields. These limitations can be overcome by engineering of wax biosynthetic pathways in the seeds of high-yielding oil crops to produce designer waxes for specific industrial end uses. In this review, we first summarize the current knowledge regarding the genes and enzymes generating the chemical diversity of cuticular waxes that accumulate at the surfaces of primary plant organs. We then consider the potential of cuticle biosynthetic genes for biotechnological wax production, focusing on selected examples of wax ester chain lengths and isomers. Finally, we discuss the genes/enzymes of cuticular alkane biosynthesis and their potential in future metabolic engineering of plants for the production of renewable hydrocarbon fuels.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.172
Teacher spread0.131 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations191
Published2008
Admission routes1
Has abstractyes

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