Global dynamics of microbial communities emerge from local interaction rules
Bibliographic record
Abstract
Abstract Interactions between cells drive biological processes across all of life, from microbes in the environment to cells in multicellular organisms. Interactions often arise in spatially structured settings, where cells mostly interact with their neighbors. A central question is how local interactions shape the properties of biological systems. This question is very relevant in the context of microbial communities, such as biofilms, where cells live close by in space and are connected via a dense network of biochemical interactions. To understand and control the functioning of these communities, it is essential to uncover how community-level properties, such as the community composition, spatial arrangement, and growth rate, arise from these interactions. Here, we develop a mathematical framework that can predict community-level properties from the molecular mechanisms underlying the cell-cell interactions for systems consisting of two cell types. Our predictions can qualitatively reproduce measurements from an experimental cross-feeding community. For these cross-feeding communities, the community growth rate is reduced when cells interact only with few neighbors; as a result, some communities can co-exist in a well-mixed system, where cells can interact with all other cells, but not in systems where cells can interact only with close by neighbors. In general, our framework shows that key molecular parameters underlying the cell-cell interactions (e.g. the uptake and leakage rates of molecules) determine community-level properties. Our framework can be extended to a variety of systems of two interacting cell types, within and beyond the microbial world, and contributes to our understanding of how community-level dynamics and biological functions emerge from microscopic interactions between single 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".