The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
A user’s guide to PDE models for chemotaxis
Open accessAbstract
Mathematical modelling of chemotaxis (the movement of biological cells or organisms in response to chemical gradients) has developed into a large and diverse discipline, whose aspects include its mechanistic basis, the modelling of specific systems and the mathematical behaviour of the underlying equations. The Keller-Segel model of chemotaxis (Keller and Segel in J Theor Biol 26:399–415, 1970; 30:225–234, 1971) has provided a cornerstone for much of this work, its success being a consequence of its intuitive simplicity, analytical tractability and capacity to replicate key behaviour of chemotactic populations. One such property, the ability to display “auto-aggregation”, has led to its prominence as a mechanism for self-organisation of biological systems. This phenomenon has been shown to lead to finite-time blow-up under certain formulations of the model, and a large body of work has been devoted to determining when blow-up occurs or whether globally existing solutions exist. In this paper, we explore in detail a number of variations of the original Keller–Segel model. We review their formulation from a biological perspective, contrast their patterning properties, summarise key results on their analytical properties and classify their solution form. We conclude with a brief discussion and expand on some of the outstanding issues revealed as a result of this work.
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Cites
Initiation of slime mold aggregation viewed as an instability
Keller, E. F., & Segel, L. A. (1970) · Journal of Theoretical Biology · doi:10.1016/0022-5193(70)90092-5 · Cited by 3,662
Mathematical Biology: II: Spatial Models and Biomedical Applications
(2003) · Interdisciplinary Applied Mathematics · doi:10.1007/b98869 · Cited by 2,293
Keller, E. F., & Segel, L. A. (1971) · Journal of Theoretical Biology · doi:10.1016/0022-5193(71)90050-6 · Cited by 1,886
Traveling bands of chemotactic bacteria: A theoretical analysis
Keller, E. F., & Segel, L. A. (1971) · Journal of Theoretical Biology · doi:10.1016/0022-5193(71)90051-8 · Cited by 1,134
Random walk with persistence and external bias
Patlak, C. S. (1953) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02476407 · Cited by 1,008
Swarms of Predators Exhibit "Preytaxis" if Individual Predators Use Area-Restricted Search
Kareiva, P., & Odell, G. (1987) · The American Naturalist · doi:10.1086/284707 · Cited by 990
Transport Equations in Biology
Perthame, B. (2007) · Frontiers in Mathematics · doi:10.1007/978-3-7643-7842-4 · Cited by 936
Models of dispersal in biological systems
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Aggregation, Blowup, and Collapse: The ABC's of Taxis in Reinforced Random Walks
Stevens, A., & Othmer, H. G. (1997) · SIAM Journal on Applied Mathematics · doi:10.1137/s0036139995288976 · Cited by 704
Complex patterns formed by motile cells of Escherichia coli
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Cited by
Srivastava, S., Kinnunen, P. C., Wang, Z., Ho, K. K. Y., Humphries, B. A., Chen, S., Linderman, J. J., Luker, G. D., Luker, K. E., & Garikipati, K. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013607 · Cited by 5
“In this work, we have chosen a simple polynomial basis, however, more sophisticated functional forms [57] could lead to more biologically robust models.”
Metadata
- Authors
- Hillen, T., & Painter, K. J.
- Venue
- Journal of Mathematical Biology
- Published
- 2008-07-15
- Volume
- 58
- Issue
- 1-2
- Pages
- 183-217
- ISSN
- 0303-6812 (ISSN-L)
- 1432-1416
- OpenAlex
- W1987347396
- Cited by
- 1,650
- References
- 48
- FWCI
- 35.64
- Type
- journal article
- Language
- en
- Open access
- green
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