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Statistical inference of the mechanisms driving collective cell movement
Author(s) -
Ferguson Elaine A.,
Matthiopoulos Jason,
Insall Robert H.,
Husmeier Dirk
Publication year - 2017
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12203
Subject(s) - dictyostelium discoideum , inference , movement (music) , computer science , sampling (signal processing) , statistical inference , advection , biological system , econometrics , ecology , artificial intelligence , biology , mathematics , statistics , physics , biochemistry , filter (signal processing) , gene , acoustics , computer vision , thermodynamics
Summary Numerous biological processes, many impacting on human health, rely on collective cell movement. We develop nine candidate models, based on advection–diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one‐dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm. The best model was selected by using the widely applicable information criterion. We conclude that cell movement in our study system was driven both by a self‐generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells.