Production of Oil in Plant Vegetative Tissues
Bibliographic record
Abstract
Synthetic biology is an emerging area of science focused on the development of well‐defined molecular components that can be assembled and utilized for rational engineering design. In many cases, the process is geared towards production of high‐value compounds, and in biofuels research, a major goal is to maximize the amount of oil that can be recovered from plants. While seeds are the traditional source of plant oil, we and others are considering the engineering of oil in plant vegetative tissues. The rationale is that the biomass of plants is dominated by leaves and stems, and as such, production of even modest amounts of oil in these tissues may significantly increase the amount of oil recovered from plants. In this seminar I will describe a variety of approaches that we are using to increase the steady‐state amount of oil in plant leaves, including enhancement of the mechanisms for oil synthesis and disruption of processes for oil breakdown. Furthermore, we have recently identified proteins involved in lipid droplet biogenesis and compartmentation, which provide additional tools for modulating the packaging and storage of lipids in plant cells. Collectively, these studies define a growing set of molecular components that can be used for lipid modification. The results of these studies are informing not only the engineering of oil content in plant leaves, but also providing new insights to oil production in plant seeds. Implications for both food and fuel will be discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".