Evidence map›Paper›PMID 40836056›Full record

ArticleScientific reports2025

Modeling bacterial growth and Allee effect via Allen-Cahn theoretical framework.

Anna Maslovskaya, Christina Kuttler, Yixuan Shuai, Alexander Chebotarev, Andrey Kovtanyuk

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

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5 · Who and what money

Authors and funding

5 authors.

Anna Maslovskaya *Institute of Data Science and Artificial Intelligence, Innopolis University, Universitetskaya st. 1, 420500, Innopolis, Russia. a.maslovskaya@innopolis.ru.
Christina Kuttler *Department of Mathematics, Technische Universität München, Boltzmannstraße 3, 85747, Garching bei München, Germany.
Yixuan Shuai *Institute of Data Science and Artificial Intelligence, Innopolis University, Universitetskaya st. 1, 420500, Innopolis, Russia.
Alexander Chebotarev *Institute for Applied Mathematics FEB RAS, Radio st. 7, 690041, Vladivostok, Russia.
Andrey Kovtanyuk *Department of Mathematics, Technische Universität München, Boltzmannstraße 3, 85747, Garching bei München, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mathematical models designed for describing, predicting, and controlling states of microbial systems promote progress in solving vital problems at the forefront of microbiology. The paper presents a theoretical justification for the modeling and in silico studies of the evolution of bacterial communities cultivated in nutrient medium. We propose a coupled approach to formalize the formation of dendrite patterns of bacteria grown in nutrient medium and the corresponding characteristics of bacterial communication. The conceptualization includes the Allen-Cahn-based model of bacterial colony evolution combined with the model of changes in biomass-dependent nutrient concentration and the reaction-diffusion model of bacterial quorum sensing. The bacterial evolution model is associated with the Landau theory within the core framework of the existence of a growth threshold for bacteria during the growth process, known as the Allee effect. The unique solvability of the initial boundary value problem is proved for the mathematical model of nutrient-dependent bacterial growth. The theoretical results are based on the derivation of new a priori estimates for the solution of the semilinear initial boundary value problem. The general mathematical model was implemented using the finite element method with the COMSOL Multiphysics platform. Various computation experiments attributed to Pseudomonas bacterial strains were performed to examine different scenarios of the spatiotemporal dynamics of key substances of the biosystem. The results obtained indicate that the considered approach can be applied to simulate the growth of dendritic bacterial colonies and to control the optimal population size above which survival is possible despite the Allee effect.

Indexed as

BacteriaModels, BiologicalModels, TheoreticalBiomassComputer SimulationPseudomonasQuorum SensingAllee effectAllen-Cahn modela priori estimatesBacterial communication modelBacterial growth modelFinite element simulationsReaction-diffusion systemUnique solvability

Identifiers

PMID40836056
PMCPMC12368270

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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.