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Toward a mathematical theory of Keller–Segel models of pattern formation in biological tissues

Bellomo, N., Bellouquid, A., Tao, Y., Winkler, M.·2015·Mathematical Models and Methods in Applied Sciences

Abstract

This paper proposes a survey and critical analysis focused on a variety of chemotaxis models in biology, namely the classical Keller–Segel model and its subsequent modifications, which, in several cases, have been developed to obtain models that prevent the non-physical blow up of solutions. The presentation is organized in three parts. The first part focuses on a survey of some sample models, namely the original model and some of its developments, such as flux limited models, or models derived according to similar concepts. The second part is devoted to the qualitative analysis of analytic problems, such as the existence of solutions, blow-up and asymptotic behavior. The third part deals with the derivation of macroscopic models from the underlying description, delivered by means of kinetic theory methods. This approach leads to the derivation of classical models as well as that of new models, which might deserve attention as far as the related analytic problems are concerned. Finally, an overview of the entire contents leads to suggestions for future research activities.

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Metadata

Authors
Bellomo, N., Bellouquid, A., Tao, Y., & Winkler, M.
Venue
Mathematical Models and Methods in Applied Sciences
Published
2015-05-28
Volume
25
Issue
09
Pages
1663-1763
ISSN
  • 0218-2025 (ISSN-L)
  • 1793-6314
OpenAlex
W2054870756
Cited by
1,077
References
52
FWCI
75.63
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Toward a mathematical theory of Keller–Segel models of pattern formation in biological tissues. https://app.cochat.ai/papers/10.1142/s021820251550044x
The paper
Bellomo, N., Bellouquid, A., Tao, Y., & Winkler, M. (2015). Toward a mathematical theory of Keller–Segel models of pattern formation in biological tissues. Mathematical Models and Methods in Applied Sciences. https://doi.org/10.1142/s021820251550044x

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.