Quantifying the intra- and inter-species community interactions in microbiomes by dynamic covariance mapping
Bibliographic record
Abstract
A microbiome's composition, stability, and response to perturbations are governed by its community interaction matrix, typically quantified through pairwise competition. However, in natural environments, microbes encounter multispecies interactions, complex conditions, and unculturable members. Moreover, evolutionary and ecological processes occur on overlapping timescales, making intra-species clonal diversity a critical but poorly understood factor influencing community interactions. Here, we present Dynamic Covariance Mapping (DCM), a general approach to infer microbiome interaction matrices from abundance time-series data. By combining DCM with high-resolution chromosomal barcoding, we quantify inter- and intra-species interactions during E. coli colonization in the mouse gut under three contexts: germ-free, antibiotic-perturbed, and innate microbiota. We identify distinct temporal phases in susceptible communities: (1) destabilization upon E. coli invasion, (2) partial recolonization of native bacteria, and (3) a quasi-steady state where E. coli sub-lineages coexist with resident microbes. These phases are shaped by specific interactions between E. coli clones and community members, emphasizing the dynamic and lineage-specific nature of microbial networks. Our results reveal how ecological and evolutionary dynamics jointly shape microbiome structure over time. The DCM framework provides a scalable method to dissect complex community interactions and is broadly applicable to bacterial ecosystems both in vitro and in situ.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".