Models

We recommend to use a virtual environment with a python 3.11 distribution to reproduce our results. Using anaconda and the environment file eulerpi_env.yml, the working virtual environment is set up through the prompt

conda env create -f eulerpi_env.yml

The environment file eulerpi_env.yml contains a human-readable list of all required dependencies. Consequently, these dependencies can also be installed manually.

In addition to this README and the environment file, the downloaded .zip ...

The folder contains the jupyter notebook for the execution of all analyses of the study. The BEST method is used in the notebook and is added in a separate python skript.

There is a class for the BEST method according to Kruschke and a class für the BEST multiple comparison.

A conda environment file with all libraries that are necessary to perform the analysis, including the package version was created. It can be easily installed via conda env create -f pymc_env.yml

The exponential decay model with all parameters, observables and conditions was specified in a yaml file.

This yaml file is converted with yaml2sbml (2020 Jakob Vanhoefer, Marta R. A. Matos, Dilan Pathirana, Yannik Schaelte and Jan Hasenauer) to a PEtab problem, which contains also the SBML model.

The SOP creates a separate SBML model for each drug and condition, as the PEtab problem contains diffrent experimental data for them.

However, the SBML models only differ in their name as for all drugs and conditions, the same exponential decay model was assumed.

The SBMLs are automatically created by yaml2sbml, when the SOP is executed. Therefore, these files are for completeness only and are not necessary to replicate the analysis.

The Folder contains:

• The MCMC and simulation results, as well as the synthetic data of the Chemical Reaction Network model (DoubleDecayIndep)
• The MCMC and simulation results, as well as the synthetic data of the Lotka-Volterra model (LotkaVolterraJoint)

Together with an executable ipynb script (Exe.ipynb) and the MCMC plotting and execution functions (MCMCFunctions.py).