The what, when, where, and why of wrinkly morphology in biofilms
Bibliographic record
Abstract
Wrinkling is a striking emergent behavior that occurs in microbial biofilms across many species. The phenomenon originates from an intricate interplay between environmental factors, cell-to-cell phenotypic heterogeneity, and mechanical forces, thus requiring insights from multiple disciplines, from biology through chemistry to physics, to be fully understood. We critically review current knowledge about wrinkle formation in biofilms, starting with an analysis of the shared and distinct features that characterize this morphology across different species and the potential evolutionary advantages associated with it. Leveraging the vast literature on Bacillus subtilis, we then focus on its biofilms to discuss in detail the molecular mechanisms and regulation of wrinkle formation, along with the environmental factors that impact this phenotype. We follow by summarizing the insights gained from theoretical and modeling work on the mechanical origin of wrinkle formation, and the related experimental studies that have attempted to measure the material properties of different biofilms. We conclude with a synthesis of the many physical, chemical, and biological factors at play and a discussion of the remaining open questions around complex architectures in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".