DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation
Bibliographic record
Abstract
DNA nanotechnology enables programmable self-assembly of nucleic acids into user-prescribed shapes and dynamics for diverse applications. This work demonstrates that concepts from DNA nanotechnology can be used to program the enzymatic activity of the phage-derived T7 RNA polymerase (RNAP) and build scalable synthetic gene regulatory networks. First, an oligonucleotide-tethered T7 RNAP is engineered via expression of an N-terminally SNAP-tagged RNAP and subsequent chemical coupling of the SNAP-tag with a benzylguanine (BG)-modified oligonucleotide. Next, nucleic-acid strand displacement is used to program polymerase transcription on-demand. In addition, auxiliary nucleic acid assemblies can be used as "artificial transcription factors" to regulate the interactions between the DNA-programmed T7 RNAP with its DNA templates. This in vitro transcription regulatory mechanism can implement a variety of circuit behaviors such as digital logic, feedback, cascading, and multiplexing. The composability of this gene regulatory architecture facilitates design abstraction, standardization, and scaling. These features will enable the rapid prototyping of in vitro genetic devices for applications such as bio-sensing, disease detection, and data storage.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".