Evidence map›Paper›PMID 42274827›Full record

ArticleBulletin of mathematical biology2026

Delayed Pattern Formation in Two-Dimensional Domains.

Nirmali Das, István Balázs, Bornali Das, Gergely Röst

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 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

4 authors.

Nirmali DasBolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged, H-6720, Hungary. nimi.szegeduni22@gmail.com.
István Balázs *Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged, H-6720, Hungary.
Bornali Das *University of Oslo, Oslo, Norway.
Gergely Röst *Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged, H-6720, Hungary.

Funding

KKP 129877National Laboratory for Health Security RRF-2.3.1-21-2022-0000National Research, Development and Innovation (NRDI) Fund FK142891National Research, Development and Innovation (NRDI) Fund, Hungary FK138924National Research, Development and Innovation (NRDI) Fund, Hungary TKP2021-EGA-05National Research, Development and Innovation Office, Hungary 2024-1.2.3-HU-RIZONT-2024-00034
6 · The paper itself

Abstract

This study investigates how the interaction between gene expression time delay and domain size governs spatio-temporal pattern formation in a reaction-diffusion system. To investigate these phenomena, we utilize a modified version of the Schnakenberg model called the LI (ligand internalisation) model. In a one-dimensional domain, a linear relationship has been observed between the gene expression time delay and the time it takes for patterns to form. We extend the model to the two-dimensional domain and confirm that a similar relationship holds there as well. However, our exploration reveals a non-monotonic correlation between domain size and the time required for pattern emergence. In our attempt to unravel these complex dynamics, we expand our analysis by considering a diverse range of initial conditions, including random perturbations of the spatially homogenous steady state as well as initial functions from its unstable manifold, to observe how pattern formation unfolds. To understand the role of these key parameters, we compute a two-parameter chart of patterns, with respect to time delay and domain size.

Indexed as

Body PatterningModels, BiologicalModels, GeneticAnimalsComputer SimulationDiffusionGene ExpressionGene Expression RegulationLigandsLinear ModelsMathematical ConceptsTime FactorsLigandsGene expressionLI modelSchnakenberg modelTime delayTuring pattern

Identifiers

PMID42274827
PMCPMC13260168

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.