The Evolution of Antibiotic Production in a Spatial Model of Bacterial Competition
Bibliographic record
Abstract
Bacteria occupy a wide range of niches with many different types coexisting. They compete directly, with some capable of producing antibiotics that kill other members of the niche. Despite this, long term survival of these ecosystems is possible. Here, we consider a lattice-based three-component system with antibiotic producers, non-producers (or cheaters), and susceptible cells competing. In our system, there is a metabolic cost tied to production rate, resulting in a decrease in growth rate for the producers. Non-producers behave as cheaters that gain the benefit of an antibiotic without the cost of producing it themselves. The susceptible cells are a faster growing different species. The model behaves in a fashion similar to the game “rock-paper-scissors", because producers beat susceptible cells, non-producers beat producers, and susceptible cells beat non-producers. We consider two spatial lattice models, one in which there is a nearest neighbour interaction between cells, and one in which the long-range diffusion of the antibiotic is explicitly included. We consider the parameter space in which the three cell types can coexist (taking into account cost and production rate), and determine the regions in which production rate is too high or too low to allow coexistence. We determine that antibiotic producers will evolve to an optimal production rate and that low-rate producers can outcompete complete cheaters (non-producers). We finally illustrate that the introduction of a fourth “resistant” cell type allows the system to survive with four members for some parameters. In other cases, addition of the resistant cells causes the extinction of the producers, which eventually favours the susceptible cells.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".