<i>In Vitro</i> Screening and <i>in Silico</i> Modeling of RNA-Based Gene Expression Control
Bibliographic record
Abstract
Molecular tools for controlling gene expression are essential for manipulating biological systems. One class of tools includes RNA switches that incorporate RNA-based sensors, known as aptamers. However, most switches reported to date are responsive to toxic molecules or to endogenous metabolites. For effective conditional control, switches must incorporate RNA aptamers that exhibit selectivity against such endogenous metabolites. We report a systematic approach which combines a rapid in vitro assay and an in silico model to support an efficient, streamlined application of aptamers into RNA switches. Model predictions were validated in vivo and demonstrate that the RNA switches enable selective and programmable gene regulation. We demonstrate the method using aptamers that bind the FDA-approved small molecule (6R)-folinic acid, providing access to new molecular targets for gene expression control and much-needed clinically relevant tools for advancing RNA-based therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".