Bibliographic record
Abstract
Populations of bacteria regulate motility through chemical signalling to control emergent self-organisation. We investigate emergent behaviour in a population of bacteria whose motility is controlled through a type of chemical signalling called quorum sensing (QS). We develop a cell-level mathematical model that accounts for both QS and genetic regulation of motility. By systematically upscaling our model with the Fokker–Planck equation, we derive two multi-scale continuum models that describe the system at the population level. Our first model uses chemical structuring to capture genetic regulation of motility. We derive our second model by effectively averaging out the chemical structure, leading to a simpler reaction-diffusion system. Through analysis and simulation of both models, we characterise different types of emergent behaviour for various broad classes of gene-regulatory networks. Examples of emergent behaviour include motility-induced phase separation, spatio-temporal oscillations, and oscillator synchronisation. We investigate qualitative and quantitative differences between the two models in order to characterise situations where chemical structure is important. This comparison provides insight on the reliability of reaction-diffusion models for motile signalling bacteria, which should benefit future modelling efforts.
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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 | 0.000 | 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 | 0.000 | 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".