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

Area of Science:

• Behavioral pharmacology
• Computational neuroscience
• Zebrafish models

Background:

• Zebrafish are widely used in preclinical research, with over 5000 papers annually, many focusing on anxiety mechanisms.
• In-silico experiments offer a promising avenue to support the 3Rs principles (replacement, reduction, refinement) in animal research.
• Understanding anxiety-related behaviors in zebrafish is crucial for advancing neuroscience research.

Purpose of the Study:

• To develop a data-driven modeling framework for predicting zebrafish anxiety-related behavioral responses.
• To simulate the effects of acute caffeine administration on zebrafish behavior.
• To provide a foundation for in-silico experiments in zebrafish behavioral pharmacology.

Main Methods:

• A two-time-scale modeling framework was developed, distinguishing slow (freezing) and fast (burst-and-coast locomotion) temporal scales.
• The model is based on Markov chain theory and stochastic differential equations.
• The framework was validated by simulating experimental observations of zebrafish treated with varying caffeine concentrations.

Main Results:

• The proposed modeling framework accurately simulates experimental observations of zebrafish behavior under different caffeine concentrations.
• The model effectively captures both freezing behavior (slow scale) and burst-and-coast locomotion (fast scale).
• The results demonstrate the framework's robustness and parsimony.

Conclusions:

• The developed data-driven model provides a robust tool for predicting zebrafish anxiety behaviors.
• This framework facilitates in-silico experiments, contributing to zebrafish research and welfare.
• The study lays the groundwork for future computational approaches in zebrafish behavioral pharmacology.