Synthetic quorum-sensing circuit to control consortial biofilm formation and dispersal in a microfluidic device
Bibliographic record
Abstract
To utilize biofilms for chemical transformations in biorefineries they need to be controlled and replaced. Previously, we engineered the global regulator Hha and cyclic diguanylate-binding BdcA to create proteins that enable biofilm dispersal. Here we report a biofilm circuit that utilizes these two dispersal proteins along with a population-driven quorum-sensing switch. With this synthetic circuit, in a novel microfluidic device, we form an initial colonizer biofilm, introduce a second cell type (dispersers) into this existing biofilm, form a robust dual-species biofilm and displace the initial colonizer cells in the biofilm with an extracellular signal from the disperser cells. We also remove the disperser biofilm with a chemically induced switch, and the consortial population could tune. Therefore, for the first time, cells have been engineered that are able to displace an existing biofilm and then be removed on command allowing one to control consortial biofilm formation for various applications. Engineered biofilms have applications in biorefineries. Here, Honget al. engineer bacteria to produce a combination of dispersal proteins and quorum sensing factors, and show that one dispersing bacterium can eliminate a biofilm formed by a colonizing bacterium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".