Data-driven modelling of social forces and collective behaviour in zebrafish

Adam K. Zienkiewicz, Fabrizio Ladu, David A.W. Barton, Maurizio Porfiri, Mario Di Bernardo

Research output: Contribution to journalArticle

Abstract

Zebrafish are rapidly emerging as a powerful model organism in hypothesis-driven studies targeting a number of functional and dysfunctional processes. Mathematical models of zebrafish behaviour can inform the design of experiments, through the unprecedented ability to perform pilot trials on a computer. At the same time, in-silico experiments could help refining the analysis of real data, by enabling the systematic investigation of key neurobehavioural factors. Here, we establish a data-driven model of zebrafish social interaction. Specifically, we derive a set of interaction rules to capture the primary response mechanisms which have been observed experimentally. Contrary to previous studies, we include dynamic speed regulation in addition to turning responses, which together provide attractive, repulsive and alignment interactions between individuals. The resulting multi-agent model provides a novel, bottom-up framework to describe both the spontaneous motion and individual-level interaction dynamics of zebrafish, inferred directly from experimental observations.

Original languageEnglish (US)
Pages (from-to)39-51
Number of pages13
JournalJournal of Theoretical Biology
Volume443
DOIs
StatePublished - Apr 14 2018

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Keywords

  • Agent-based modelling
  • Data-driven
  • Stochastic differential equations
  • Zebrafish

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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