Parameter Estimation and Model Selection for the Quantitative Analysis of Oncolytic Virus Therapy in Zebrafish Embryos

Overview

This repository contains the code and supplementary accompanying the paper, Parameter Estimation and Model Selection for the Quantitative Analysis of Oncolytic Virus Therapy in Zebrafish Embryos (submitted to IFAC DYCOPS 2025)

Repository Contents

• data/: contains the original tumor volume measurements from Mealiea et al. (2021)
• figure/: contains figure outputs from visualization.ipynb for each model, each subfolder containing:
  • model fits
  • profile plots
  • waterfall plot combined with parameter plots
• model/: contains the proof of Lipschitz continuity for all models and the following three models listed in the paper:
  • baseline model
  • age-of-infection model
  • individual-based age-of-infection model

in each model folder, there are files:

• README.md: structural identifiability information
• model_creation.py: create the .xml model file
• petab_files_creation.ipynb: build petab files defining the optimization problem
• model_optimization.py: perform optimization
• visualization.ipynb: visualize the optimization results
• check_gradients.ipynb: double check the gradients of the model

Requirement

After installing all the code using git clone https://github.com/EchoRLiu/OV.git, run the following command in the project folder to creat a virtual environment and install all the necessary packages

python -m venv env

source env/bin/activate

pip install -v -r requirements.txt

Tutorial

Quickstart

To try one of the models,

1. first go to the model folder, e.g. individual_based_age_of_infection_model, and perform model optimization and save the printouts (if there is no optimization_history folder, please create one first to store all the results):

cd model/individual_based_age_of_infection_model

python model_optimization.py > optimization_history/result.out 2>&1

2. run through visualization.ipynb to visualize the results

3. (optional) run through the check_gradients.ipynb to check the gradients

Contact

If you have any questions, please feel free to contact any of the authors:

• Yuhong Liu ([email protected])
• Dilan Pathirana ([email protected])
• Jan Hasenauer ([email protected])

or create an issue