Evidence map›Paper›PMID 42331812›Full record

ArticleNPJ biofilms and microbiomes2026

Agent-based modeling of erosion and sloughing during growth of Pseudomonas aeruginosa biofilms.

Ryan Bournes, Suzie Hingley-Wilson, Bing Guo, Cayla Harris, Umar Abubacar, Mark Chambers, Roman Bauer

Abstract read
In one paragraph

Article in NPJ biofilms and microbiomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ryan BournesComputer Science Research Centre, University of Surrey, Guildford, UK. rb00831@surrey.ac.uk.
Suzie Hingley-WilsonSchool of Biosciences, University of Surrey, Guildford, UK.
Bing GuoSchool of Sustainability, Civil and Environmental Engineering, University of Surrey, Guildford, UK.
Cayla HarrisComputer Science Research Centre, University of Surrey, Guildford, UK.
Umar AbubacarComputer Science Research Centre, University of Surrey, Guildford, UK.
Mark ChambersSchool of Biosciences, University of Surrey, Guildford, UK.
Roman BauerComputer Science Research Centre, University of Surrey, Guildford, UK. r.bauer@surrey.ac.uk.

Funding

Engineering and Physical Sciences Research Council 2746335
6 · The paper itself

Abstract

Pseudomonas aeruginosa is a prevalent healthcare pathogen. It forms biofilms, which significantly increase antibiotic resistance, escalating the burden on healthcare systems. While global biofilm phenomena are observable experimentally, understanding local behaviours of bacteria and their environment, and how these factors drive biofilm formation, is challenging. Agent-based modelling (ABM) offers a computational solution to simulate these local behaviours and resulting large-scale phenomena. ABMs can simulate physical mechanisms like erosion and sloughing but often lack the ability to capture them during biofilm growth and validate them experimentally. We present a three-dimensional ABM of P. aeruginosa biofilms growing under constant flow. This model is the first of its kind to accurately capture the change in physical shape of biofilms growing under constant flow, as well as capture erosion and sloughing as natural byproducts of local bacteria interactions. The model is validated both qualitatively and quantitatively against experimental culture results, providing a general framework that can be adapted to simulate different environments or other biofilm-forming species.

Indexed as

BiofilmsModels, BiologicalPseudomonas aeruginosaComputer Simulation

Identifiers

PMID42331812
PMCPMC13669389

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.