Data-driven stochastic modelling of zebrafish locomotion

Adam K Zienkiewicz, David A W Barton, Maurizio Porfiri, Mario Di Bernardo*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

31 Citations (Scopus)
489 Downloads (Pure)

Abstract

In this work, we develop a data-driven modelling framework to reproduce the locomotion of fish in a confined environment. Specifically, we highlight the primary characteristics of the motion of individual zebrafish (Danio rerio), and study how these can be suitably encapsulated within a mathematical framework utilising a limited number of calibrated model parameters. Using data captured from individual zebrafish via automated visual tracking, we develop a model using stochastic differential equations and describe fish as a self propelled particle moving in a plane. Based on recent experimental evidence of the importance of speed regulation in social behaviour, we extend stochastic models of fish locomotion by introducing experimentally-derived processes describing dynamic speed regulation. Salient metrics are defined which are then used to calibrate key parameters of coupled stochastic differential equations, describing both speed and angular speed of swimming fish. The effects of external constraints are also included, based on experimentally observed responses. Understanding the spontaneous dynamics of zebrafish using a bottom-up, purely data-driven approach is expected to yield a modelling framework for quantitative investigation of individual behaviour in the presence of various external constraints or biological assays.

Original languageEnglish
Pages (from-to)1081-1105
Number of pages25
JournalJournal of Mathematical Biology
Volume71
Issue number5
Early online date31 Oct 2014
DOIs
Publication statusPublished - 1 Nov 2015

Keywords

  • Fish locomotion
  • Computational biology
  • Stochastic models
  • Zebrafish
  • Ornstein
  • Uhlenbeck

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