Engineering coupled consortia-based biosensors for diagnostic
Bibliographic record
Abstract
Synthetic multicellular systems have great potential for performing complex tasks, including multi-signal detection and computation through cell-to-cell communication. However, engineering these systems is challenging, requiring precise control over the cell concentrations of distinct members and coordination of their activity. Here, we develop a bacterial consortia-based biosensor for Heme and Lactate, wherein members are coupled through a global shared quorum-sensing signal that simultaneously controls the activity of the diverse biosensing strains. The multicellular system incorporates a gene circuit that computes the minimum between each biosensor’s activity and the shared signal. We evaluate three consortia configurations: one where the shared signal is externally supplied, another directly produced via an inducible gene circuit, and a third generated through an incoherent feedforward loop (IFFL) gene circuit. Among these configurations, the IFFL system, which maintains the shared signal at low and stable levels over an extended period, demonstrates improved performance and robustness against perturbations in cell populations. Finally, we examine these coupled consortia to monitor Lactate and Heme in humanized fecal samples for diagnostics. Synthetic multicellular systems have great potential for performing complex tasks, including multi-signal detection and computation through cell-to-cell communication. Here the authors engineer microbial consortia to detect heme and lactate in fecal samples, demonstrating that coupled consortia, where members are aware of the presence of the other, exhibited enhanced diagnostic performance.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".