Phenotypic plasticity shapes biofilm’s structure and fluid transport enhancing resilience
Bibliographic record
Abstract
Abstract Phenotypic heterogeneity is one of the hallmarks of the biofilm lifestyle, where even isogenic populations give rise to spatially organized and phenotypically distinct subpopulations. One such pattern is generated by the ability of several biofilm-forming bacteria to switch between a flagellated and a matrix producing state. Here, using Bacillus subtilis as a model system, we investigate the role of this switch during biofilm development on a solid-air interface. By comparing the matrix-flagella spatio-temporal patterns in wild-type biofilms with mixtures of flagella- and matrix-null mutants biofilms, we find that pattern formation does not require a phenotypic switch that enables individual cells to respond to the local environment, but can be explained by a completely stochastic switch coupled to a phenotype-dependent fitness landscape that selects phenotypes at the population level. Integration of experiments and physical models shows that the coexistence between flagellated and matrix-producing cells provides the population with enhanced resilience to environmental changes, by enabling cells to manipulate and harness the local morphological and transport properties within the biofilm. Our results not only reveal a new evolutionary advantage of phenotypic plasticity in biofilms, but also illustrate how the biology and ecology of these populations are intrinsically tied to their physical properties.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".