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Discrete Element Framework for Modelling Extracellular Matrix, Deformable Cells and Subcellular Components

Overview of attention for article published in PLoS Computational Biology, October 2015
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Title
Discrete Element Framework for Modelling Extracellular Matrix, Deformable Cells and Subcellular Components
Published in
PLoS Computational Biology, October 2015
DOI 10.1371/journal.pcbi.1004544
Pubmed ID
Authors

Bruce S Gardiner, Kelvin K L Wong, Grand R Joldes, Addison J Rich, Chin Wee Tan, Antony W Burgess, David W Smith

Abstract

This paper presents a framework for modelling biological tissues based on discrete particles. Cell components (e.g. cell membranes, cell cytoskeleton, cell nucleus) and extracellular matrix (e.g. collagen) are represented using collections of particles. Simple particle to particle interaction laws are used to simulate and control complex physical interaction types (e.g. cell-cell adhesion via cadherins, integrin basement membrane attachment, cytoskeletal mechanical properties). Particles may be given the capacity to change their properties and behaviours in response to changes in the cellular microenvironment (e.g., in response to cell-cell signalling or mechanical loadings). Each particle is in effect an 'agent', meaning that the agent can sense local environmental information and respond according to pre-determined or stochastic events. The behaviour of the proposed framework is exemplified through several biological problems of ongoing interest. These examples illustrate how the modelling framework allows enormous flexibility for representing the mechanical behaviour of different tissues, and we argue this is a more intuitive approach than perhaps offered by traditional continuum methods. Because of this flexibility, we believe the discrete modelling framework provides an avenue for biologists and bioengineers to explore the behaviour of tissue systems in a computational laboratory.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 3%
Netherlands 1 1%
Taiwan 1 1%
Unknown 69 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 25%
Researcher 11 15%
Student > Master 10 14%
Student > Bachelor 5 7%
Student > Postgraduate 3 4%
Other 8 11%
Unknown 18 25%
Readers by discipline Count As %
Engineering 18 25%
Agricultural and Biological Sciences 15 21%
Biochemistry, Genetics and Molecular Biology 6 8%
Computer Science 3 4%
Physics and Astronomy 3 4%
Other 7 10%
Unknown 21 29%