Evolutionary theory of bacterial quorum sensing: when is a signal not a signal?
Bibliographic record
Abstract
The term quorum sensing (QS) is used to describe the communication between bacterial cells, whereby a coordinated population response is controlled by diffusible molecules produced by individuals. QS has not only been described between cells of the same species (intraspecies), but also between species (interspecies) and between bacteria and higher organisms (inter-kingdom). The fact that QS-based communication appears to be widespread among microbes is strange, considering that explaining both cooperation and communication are two of the greatest problems in evolutionary biology. From an evolutionary perspective, intraspecies signalling can be explained using models such as kin selection, but when communication is described between species, it is more difficult to explain. It is probable that in many cases this involves QS molecules being used as 'cues' by other species as a guide to future action or as manipulating molecules whereby one species will 'coerce' a response from another. In these cases, the usage of QS molecules cannot be described as signalling. This review seeks to integrate the evolutionary literature on animal signalling with the microbiological literature on QS, and asks whether QS within bacteria is true signalling or whether these molecules are also used as cues or for the coercion of other cells.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".