Evidence map›Paper›PMID 26452000›Full record

ArticlePLoS computational biology2015

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

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

Abstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
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  7. Physical models of collective cell motility: from cell to tissue.Journal of physics D: Applied physics · 2017
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Bruce S GardinerSchool of Engineering and Information Technology, Murdoch University, Perth, Australia.
Kelvin K L WongEngineering Computational Biology, School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
Grand R JoldesIntelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Perth, Australia.
Addison J RichEngineering Computational Biology, School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
Chin Wee TanStructural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia.
Antony W BurgessStructural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia; Department of Surgery, Royal Melbourne Hospital, Melbourne, Victoria, Australia.
David W SmithEngineering Computational Biology, School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Cell Physiological PhenomenaModels, BiologicalAnimalsComputer SimulationExtracellular MatrixHumansMechanotransduction, CellularModels, StatisticalSubcellular Fractions

Identifiers

PMID26452000
PMCPMC4599884

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.