↓ Skip to main content

A user’s guide to PDE models for chemotaxis

Overview of attention for article published in Journal of Mathematical Biology, July 2008
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)

Mentioned by

twitter
3 X users
wikipedia
2 Wikipedia pages
f1000
1 research highlight platform

Citations

dimensions_citation
1185 Dimensions

Readers on

mendeley
347 Mendeley
citeulike
2 CiteULike
Title
A user’s guide to PDE models for chemotaxis
Published in
Journal of Mathematical Biology, July 2008
DOI 10.1007/s00285-008-0201-3
Pubmed ID
Authors

T. Hillen, K. J. Painter

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 347 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
United Kingdom 3 <1%
India 3 <1%
Germany 2 <1%
France 2 <1%
Netherlands 2 <1%
Canada 2 <1%
Australia 1 <1%
United Arab Emirates 1 <1%
Other 2 <1%
Unknown 323 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 108 31%
Researcher 61 18%
Student > Master 32 9%
Professor 30 9%
Professor > Associate Professor 17 5%
Other 52 15%
Unknown 47 14%
Readers by discipline Count As %
Mathematics 114 33%
Agricultural and Biological Sciences 43 12%
Physics and Astronomy 35 10%
Engineering 33 10%
Biochemistry, Genetics and Molecular Biology 18 5%
Other 44 13%
Unknown 60 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2022.
All research outputs
#4,144,328
of 22,660,862 outputs
Outputs from Journal of Mathematical Biology
#69
of 655 outputs
Outputs of similar age
#13,707
of 81,073 outputs
Outputs of similar age from Journal of Mathematical Biology
#1
of 2 outputs
Altmetric has tracked 22,660,862 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 655 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 81,073 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them