An autonomous microbial sensor enables long-term detection of TNT explosive in natural soil
Bibliographic record
Abstract
Microbes can be engineered to sense target chemicals for environmental and geospatial detection. However, when engineered microbes operate in real-world environments, it remains unclear how competition with natural microbes affect their performance over long time periods. Here, we engineer sensors and memory-storing genetic circuits inside the soil bacterium Bacillus subtilis to sense the TNT explosive and maintain a long-term response, using predictive models to design riboswitch sensors, tune transcription rates, and improve the genetic circuit’s dynamic range. We characterize the autonomous microbial sensor’s ability to detect TNT in a natural soil system, measuring single-cell and population-level behavior over a 28-day period. The autonomous microbial sensor activates its response by 14-fold when exposed to low TNT concentrations and maintains stable activation for over 21 days, exhibiting exponential decay dynamics at the population-level with a half-life of about 5 days. Overall, we show that autonomous microbial sensors can carry out long-term detection of an important chemical in natural soil with competitive growth dynamics serving as additional biocontainment. Engineered microbes can detect harmful chemicals, but may not work well in complex environments. Here, the authors built microbial sensors for detection of TNT explosive and tested their response over 28 days in contaminated soil with many natural microbes, achieving stable detection for 21 days.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".