Silk Purse from a Sow’s Ear? Spider Silk Production in Tobacco
Bibliographic record
Abstract
Dragline spider silk is nature’s strongest known fiber and, since it compares well with many synthetic fibers, it has great potential for use in a wide array of industrial and medical applications that range from surgical sutures to bulletproof vests. Spiders use specialized glands to produce as many as seven different types of silk, and they tailor them to diverse uses such as web construction, egg sacks, draglines, and cocoons. Dragline silk, which forms the scaffold of the spider’s web, is the strongest of all types and is even tougher than Kevlar. Composed of two different proteins, dragline silk is located inside spiders ’ silk glands in a liquid crystalline solution known as “spinning dope. ” This silk protein solution can be transformed to a thread through a series of steps, including protein molecule orientation, ion exchange, pH gradient, water removal, and drawing, all of which happen naturally in the spider’s spinneret. Despite having this knowledge, it has not been possible to make fabrics from spider silk simply because spiders can’t be “farmed ” and there is no other concentrated source for spinning. So if we want to make such fabrics, we need to duplicate spider silk in some other system. Development of machinery to do the spinning can be accomplished through engineering. The production of recombinant dragline silk proteins in transgenic plants, in a process known as “molecular farming, ” can provide the spinning dope. In fact, the two components of spinning dope have been produced in a collaboration between Agriculture and AgriFood Canada and Nexia Biotechnologies. They have shown that the two essential protein components of dragline spider silk, known as major ampullate spidroin proteins 1 and 2, can be produced in transgenic tobacco1. Two synthetic genes,
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".