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Statistical modelling of cell movement

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  • Diana Giurghita
  • Dirk Husmeier

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.

Suggested Citation

  • Diana Giurghita & Dirk Husmeier, 2018. "Statistical modelling of cell movement," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 265-280, August.
    • Handle: RePEc:bla:stanee:v:72:y:2018:i:3:p:265-280
    DOI: 10.1111/stan.12140
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    References listed on IDEAS

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    1. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    2. Mohsen Bahmani-Oskooee & Ford Brown, 2004. "Kalman filter approach to estimate the demand for international reserves," Applied Economics, Taylor & Francis Journals, vol. 36(15), pages 1655-1668.
    3. Elaine A. Ferguson & Jason Matthiopoulos & Robert H. Insall & Dirk Husmeier, 2017. "Statistical inference of the mechanisms driving collective cell movement," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 869-890, August.
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