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Inference of the drivers of collective movement in two cell types:Dictyosteliumand melanoma
Author(s) -
Elaine A. Ferguson,
Jason Matthiopoulos,
Robert H. Insall,
Dirk Husmeier
Publication year - 2016
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2016.0695
Subject(s) - inference , movement (music) , dictyostelium discoideum , computer science , biological system , set (abstract data type) , biology , biochemical engineering , neuroscience , artificial intelligence , physics , biochemistry , acoustics , engineering , gene , programming language
Collective cell movement is a key component of many important biological processes, including wound healing, the immune response and the spread of cancers. To understand and influence these movements, we need to be able to identify and quantify the contribution of their different underlying mechanisms. Here, we define a set of six candidate models-formulated as advection-diffusion-reaction partial differential equations-that incorporate a range of cell movement drivers. We fitted these models to movement assay data from two different cell types: Dictyostelium discoideum and human melanoma. Model comparison using widely applicable information criterion suggested that movement in both of our study systems was driven primarily by a self-generated gradient in the concentration of a depletable chemical in the cells' environment. For melanoma, there was also evidence that overcrowding influenced movement. These applications of model inference to determine the most likely drivers of cell movement indicate that such statistical techniques have potential to support targeted experimental work in increasing our understanding of collective cell movement in a range of systems.

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