From single cells to communities: Mathematical perspectives on bacterial quorum sensing
Bibliographic record
Abstract
The complex social lives of microbes, defined by sophisticated communication networks and cooperative behaviors, play a pivotal role in their survival and adaptation. Central to these interactions is Quorum Sensing (QS), a mechanism widely used by bacterial species for population-scale communication and subsequent gene regulation. QS involves coordinated synthesis, release, and detection of signaling molecules known as autoinducers (AIs). By sensing the concentration of AI as a proxy for the local density of the bacterial population, QS orchestrates the modulation of target gene expression. The molecular foundations of QS have been revealed, but fundamental quantitative challenges remain in the pursuit of a complete understanding of QS dynamics. Mathematical modeling has become an essential tool for investigating QS dynamics at both single-cell and population levels. A variety of modeling approaches, including deterministic, stochastic, non-spatial, and spatial frameworks have been employed to explore the complexities of QS systems. We provide an overview of mathematical models developed to describe and analyze QS mechanisms and dynamics, highlighting their contributions and limitations. Looking ahead, QS modeling is poised to support synthetic biology, antimicrobial therapy, environmental management, and more, offering new strategies to manipulate bacterial behavior for improving biotechnological applications, combating infections, and optimizing industrial processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".