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

Authored by Bernardo Mario di, Adam K Zienkiewicz, Fabrizio Ladu, David A W Barton, Maurizio Porfiri

Date Published: 2018

DOI: 10.1016/j.jtbi.2018.01.011

Sponsors: United Kingdom Engineering and Physical Sciences Research Council (EPSRC) United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

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. (C) 2018 Elsevier Ltd. All rights reserved.

Tags

Agent-based modelling movement Data-driven Zebrafish Stochastic differential equations Animal groups Rules Fish schools Locomotion Danio-rerio Ontogeny