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A space-jump derivation for non-local models of cell–cell adhesion and non-local chemotaxis

Overview of attention for article published in Journal of Mathematical Biology, June 2017
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Title
A space-jump derivation for non-local models of cell–cell adhesion and non-local chemotaxis
Published in
Journal of Mathematical Biology, June 2017
DOI 10.1007/s00285-017-1144-3
Pubmed ID
Authors

Andreas Buttenschön, Thomas Hillen, Alf Gerisch, Kevin J. Painter

Abstract

Cellular adhesion provides one of the fundamental forms of biological interaction between cells and their surroundings, yet the continuum modelling of cellular adhesion has remained mathematically challenging. In 2006, Armstrong et al. proposed a mathematical model in the form of an integro-partial differential equation. Although successful in applications, a derivation from an underlying stochastic random walk has remained elusive. In this work we develop a framework by which non-local models can be derived from a space-jump process. We show how the notions of motility and a cell polarization vector can be naturally included. With this derivation we are able to include microscopic biological properties into the model. We show that particular choices yield the original Armstrong model, while others lead to more general models, including a doubly non-local adhesion model and non-local chemotaxis models. Finally, we use random walk simulations to confirm that the corresponding continuum model represents the mean field behaviour of the stochastic random walk.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 36%
Student > Ph. D. Student 4 16%
Student > Master 4 16%
Professor 1 4%
Lecturer > Senior Lecturer 1 4%
Other 1 4%
Unknown 5 20%
Readers by discipline Count As %
Mathematics 11 44%
Engineering 3 12%
Physics and Astronomy 2 8%
Chemical Engineering 1 4%
Agricultural and Biological Sciences 1 4%
Other 3 12%
Unknown 4 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 15 June 2017.
All research outputs
#14,941,384
of 22,981,247 outputs
Outputs from Journal of Mathematical Biology
#312
of 658 outputs
Outputs of similar age
#188,673
of 317,336 outputs
Outputs of similar age from Journal of Mathematical Biology
#7
of 13 outputs
Altmetric has tracked 22,981,247 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 658 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 50% 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 317,336 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.