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Record W4414716337 · doi:10.1038/s44431-025-00007-4

The interplay of adhesion, friction, and nutrient availability in modulating biofilm wrinkling behavior

2025· article· en· W4414716337 on OpenAlexaff
Akhilesh Kumar Verma, Abhirup Mookherjee, Carolina Tropini, Diana Fusco, Luis Ruiz Pestana

Bibliographic record

Venuenpj Soft Matter · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Advanced Research
FundersHuman Frontier Science Program
KeywordsBiofilmAdhesionNutrientInstabilityBucklingStress (linguistics)Strain (injury)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.233
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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