Genetic “expiry-date” circuits control lifespan of synthetic scavenger bacteria for safe bioremediation
Bibliographic record
Abstract
Synthetic biology enabled the systematic engineering of bacteria for diverse applications, but their deployment in open environments raises concerns about their persistence and unintended ecological impacts. To address these challenges, genetic "expiry-date" circuits were designed to impose a tunable lifespan on bacteria. These circuits, structured as a feedforward activation network, regulate the timing of cell death by controlling the expression of Lysis E, enabling a programmed lifespan ranging from hours to days. The lifespan can be tailored by modifying the number of activation steps in the cascade. The circuits were optimized by reducing gene expression leakiness of Lysis E using a synthetic small regulatory RNA and combining it with an asd-based auxotrophic system. The bacteria harboring the "expiry-date" circuits resulted in a GMO escape rate below U.S. NIH release standards (<10-10). To validate the practical applicability of this system, a synthetic phenol-scavenging Escherichia coli was constructed, which possessed enhanced phenol tolerance and phenol-detoxification capability, and harbored the "expiry-date" circuits. The engineered bacteria detoxified 0.1 g/kg of phenol in soil within 4 days and self-destructed by day 5. These results support the circuit's potential as a biocontainment strategy for the safe and controlled deployment of synthetic bacteria in real-world applications.
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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".