Chemostat modeling of<i>Escherichia coli</i>persistence in conventionalized mono-associated and streptomycin-treated mice
Bibliographic record
Abstract
We have previously shown that Escherichia coli BJ4 has similar doubling time in mice that are mono-associated (having only the inoculated E. coli BJ4) or streptomycin-treated (having mainly gram-positive bacteria plus the inoculated E. coli BJ4). We also showed that when the mice were conventionalized (fed cecum homogenate from conventional mice or ones with a complete microbial flora), the introduction of complete flora in both cases increased the in vivo doubling time, while decreasing the colony counts in fecal samples. To determine whether the increase in doubling time could explain the decrease in colony counts, we analyzed our previous results by a chemostat model. The analysis shows that the increasing doubling time alone is sufficient to explain the decrease in colony counts in mono-associated mice, but not in the streptomycin-treated mice. The observed decreasing rate in colony counts in streptomycin-treated mice is slower than predicted. Furthermore, whereas the model predicted a decrease to extinction in both mice, the E. coli persist at a frequency 10-80 times higher in streptomycin-treated mice than in mono-associated mice. Thus, while a chemostat model is able to explain some of the population dynamics of intestinal bacteria in mice, additional factors not included in the model are stabilizing the system. Because we find that E. coli declines more slowly and to a higher stabilization frequency in streptomycin-treated mice, which have a more diverse flora before conventionalization, we take these results to suggest that the persistence of E. coli populations is promoted by species diversity. We propose that a mechanism for the persistence may be the presence of new E. coli niches created by keystone species in the more diverse flora.
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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".