Cell-free transcriptional regulation via nucleic-acid-based transcription factors
Bibliographic record
Abstract
Abstract Cells execute complex transcriptional programs by deploying distinct protein regulatory assemblies that interact with cis-regulatory elements throughout the genome. Using concepts from DNA nanotechnology, we synthetically recapitulated this feature in cell-free gene networks actuated by T7 RNA polymerase (RNAP). Our approach involves engineering nucleic-acid hybridization interactions between a T7 RNAP site-specifically functionalized with single-stranded DNA (ssDNA), templates displaying cis-regulatory ssDNA domains, and auxiliary nucleic-acid assemblies acting as artificial transcription factors (TFs). By relying on nucleic-acid hybridization, de novo regulatory assemblies can be computationally designed to emulate features of protein-based TFs, such as cooperativity and combinatorial binding, while offering unique advantages such as programmability, chemical stability, and scalability. We illustrate the use of nucleic-acid TFs to implement transcriptional logic, cascading, feedback, and multiplexing. This framework will enable rapid prototyping of increasingly complex in vitro genetic devices for applications such as portable diagnostics, bio-analysis, and the design of adaptive materials.
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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 | 0.000 |
| 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".