Data-driven modeling of zebrafish behavioral response to acute caffeine administration

Daniel A. Burbano-L., Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review


Over the last thirty years, we have witnessed a dramatic rise in the use of zebrafish in preclinical research. Every year, more than 5000 technical papers are published about zebrafish, many of them seeking to explain the underpinnings of anxiety through animal testing. In-silico experiments could significantly contribute to zebrafish research and welfare, by offering new means to support the 3Rs principles of replacement, reduction, and refinement. Here, we propose a data-driven modeling framework to predict the anxiety-related behavioral response of zebrafish to acute caffeine administration. The modeling framework unfolds along a two-time-scale dichotomy to capture freezing behavior along a slow temporal scale and burst-and-coast locomotion at a fast time-scale. Anchored in the theory of Markov chains and stochastic differential equations, we demonstrate a parsimonious, yet robust, modeling framework to accurately simulate experimental observations of zebrafish treated at different caffeine concentrations. Our results complement recent modeling efforts, laying the foundations for conducting in-silico experiments in zebrafish behavioral pharmacology.

Original languageEnglish (US)
Article number110054
JournalJournal of Theoretical Biology
StatePublished - Jan 21 2020


  • Anxiety
  • Danio rerio
  • In-silico
  • Pharmacology
  • Stochastic differential equation

ASJC Scopus subject areas

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


Dive into the research topics of 'Data-driven modeling of zebrafish behavioral response to acute caffeine administration'. Together they form a unique fingerprint.

Cite this