Engineered signal-coupled inducible promoters: measuring the apparent RNA-polymerase resource budget
Bibliographic record
Abstract
Inducible promoters are a central regulatory component in synthetic biology, metabolic engineering, and protein production for laboratory and commercial uses. Many of these applications utilize two or more exogenous promoters, imposing a currently unquantifiable metabolic burden on the living system. Here, we engineered a collection of inducible promoters (regulated by LacI-based transcription factors) that maximize the free-state of endogenous RNA polymerase (RNAP). We leveraged this collection of inducible promotors to construct simple two-channel logical controls that enabled us to measure metabolic burden - as it relates to RNAP resource partitioning. The two-channel genetic circuits utilized sets of signal-coupled transcription factors that regulate cognate inducible promoters in a coordinated logical fashion. With this fundamental genetic architecture, we evaluated the performance of each inducible promoter as discrete operations, and as coupled systems to evaluate and quantify the effects of resource partitioning. Obtaining the ability to systematically and accurately measure the apparent RNA-polymerase resource budget will enable researchers to design more robust genetic circuits, with significantly higher fidelity. Moreover, this study presents a workflow that can be used to better understand how living systems adapt RNAP resources, via the complementary pairing of constitutive and regulated promoters that vary in strength.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".