Zebrafish is emerging as a species of choice in alcohol-related pharmacological studies. In these studies, zebrafish are often exposed to acute ethanol treatments and their activity scored during behavioral assays. Computational modeling of zebrafish behavior is expected to positively impact these efforts by offering a predictive toolbox to plan hypothesis-driven studies, reduce the number of subjects, perform pilot trials, and refine behavioral screening. In this work, we demonstrate the use of the recently proposed jump persistent turning walker to model the turning rate dynamics of zebrafish exposed to acute ethanol administration. This modeling framework is based on a stochastic mean reverting jump process to capture the sudden and large changes in orientation of swimming zebrafish. The model is calibrated on an available experimental dataset of 40 subjects, tested at different ethanol concentrations. We demonstrate that model parameters are modulated by ethanol administration, whereby both the relaxation rate and jump frequency of the turning rate dynamics are influenced by ethanol concentration. This effort offers a first evidence for the possibility of complementing zebrafish pharmacological research with computational modeling of animal behavior.
- stochastic differential equations
- turning rate
ASJC Scopus subject areas
- Applied Mathematics
- Modeling and Simulation