Strain displacement in microbiomes via ecological competition
Bibliographic record
Abstract
Microorganisms commonly live in diverse communities where changes in composition can be critical for health, industry and the environment. Yet, what enables one strain to competitively replace another in these complex conditions remains poorly understood. Here we develop a mathematical model to determine general principles of strain displacement. Our modelling reveals that weak resource competition enables successful invasion while strong interference competition, for example, via antimicrobial production, enables successful displacement. We verify these predictions using in vitro assays with genetically engineered Escherichia coli. We then apply our principles to displace multidrug-resistant clinical isolates using strains that are equipped with a potent bacteriocin. Finally, we perform experiments with diverse human gut symbionts, which reveal that displacement relies on low resource competition not only between competing strains but also with the broader community, that is, limited nutrient blocking. These general rules for ecological success in microbial communities could be applied for targeted displacement of bacteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".