Towards light‐induced control of gene expression using RNA
Bibliographic record
Abstract
The potential utility of catalytic RNAs and DNAs (ribozymes and deoxyribozymes, respectively) as reagents in molecular biology as well as therapeutic agents for a variety of human diseases, has long been recognized. Although naturally occurring RNA‐cleaving ribozymes are typically not subject to feedback control, rational methodologies for the creation of allosteric ribozymes, by functional combination of ribozyme and ligand‐responsive aptamer RNA elements, have existed for some years. I will describe the in vitro selection of RNA aptamers specific for binding one but not the other of two light‐induced isomers of a dihydrobenzo[e]pyrene “photochromic switch” compound, and the utilization of such an aptamer for the construction of the UG‐dihydropyrene ribozyme, an allosteric hammerhead ribozyme whose catalysis is controllable by irradiation with visible versus ultraviolet light. In the presence of micromolar concentrations of the photo‐switch compound, the ribozyme behaves as a two‐state switch, exhibiting a > 900‐fold difference in catalytic rates between the two irradiation regimes. I will then describe our efforts to use this RNA‐based system to control gene expression within yeast cells.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".