The interplay of adhesion, friction, and nutrient availability in modulating biofilm wrinkling behavior
Bibliographic record
Abstract
Wrinkled patterns in biofilms arise from buckling instabilities triggered by stresses that accumulate as growth is constrained by a stationary substrate. While nutrient availability, friction, and adhesion each influence wrinkling, their combined effects remain poorly understood. Here, we address this gap using a lattice-network model of biofilm morphogenesis. Under constant nutrient supply, wrinkles initiate at the center, where stresses are highest and isotropic, regardless of the level of friction or adhesion. Stronger adhesion delays wrinkling and decouples the length scale governing the buckling instability from the overall biofilm size. Heterogeneous adhesion lowers the critical stress by triggering wrinkles in weakly adhered regions, with the effect modulated by friction and the average adhesion. Under nonuniform nutrient supply, our model predicts that wrinkle initiation shifts from the center to the edge as initial nutrient availability decreases, a transition we experimentally validate using E. coli biofilms.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".