Statistical modelling of cell movement

Giurghita, D. and Husmeier, D. (2018) Statistical modelling of cell movement. Statistica Neerlandica, 72(3), pp. 265-280. (doi:10.1111/stan.12140)

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Abstract

Collective cell movement affects vital biological processes in the human organism such as wound healing, immune response, and cancer metastasis. A better understanding of the mechanisms driving cell movement is then essential for the advancement of medical treatments. In this paper, we demonstrate how the unscented Kalman filter, a technique used extensively in engineering in the context of filtering, can be applied to estimate random or directed cell movement. Our proposed model, formulated using stochastic differential equations, is fitted on data describing the movement of Dictyostelium cells, an amoeba routinely used as a proxy for eukaryotic cell movement.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Giurghita, Miss Diana
Authors: Giurghita, D., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Statistica Neerlandica
Publisher:Wiley
ISSN:0039-0402
ISSN (Online):1467-9574
Published Online:15 April 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Statistica Neerlandica 72(3): 265-280
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
694461EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancerRaymond OgdenEngineering and Physical Sciences Research Council (EPSRC)EP/N014642/1M&S - MATHEMATICS