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 ...

HSD11B1 inhibition by AZD4017 and the effect on cortisone and 11KA4 metabolism was simulated in Mathematica. Figure 6 of the manuscript is reproduced in the notebook.

Mathematica notebook for simulation of combined effect of HSD11B1/AKR1C3 ratio variation and HSD11B1 inhibition, surface plots are generated shown in Fig. 4 of the manuscript.

HSD11B1 was inhibited by CBX and the effect on cortisone and 11KA4 conversion was simulated. Model simulated in Mathematica, Figure 3 panels are presented.

Mathematica notebook with model simulation of metabolite profiles after 24h incubation with different ratios of HSD11B1 and AKR1C3 transfected HEK293 cells.

First version of Genome-scale metabolic model (GEM) for reconstraction of flavonoids biosynthetic pathways. This model includes as a chassis , the Pseudomonas Putida GEM (iJN1411) . It includes the metabolic reconstruction of more than 500 flavonoids and more than 500 reactions related to the flavonoid biosynthesis.

This folder contains the python code for developing weighted loss trainer (WeLT) with all the expiremnatal work. Here is the link for our public [GitHub repository] (https://github.com/mobashgr/WELT.git).

The FEM models how metabolic slowdown will induce the age-related changes of weight gain, insulin resistance, basal inflammation, mitochondrial dysfunction, as well as the age-related disease of atherosclerosis, via a series of unavoidable homeostatic shifts.

Underlying R script for the investigation of immune cells. Script contains basic data processing, as well as a DE and monocle analysis.

Adjusted model to test the model's ability to oxygen consumption rate by permeabilised HepG2 cells in an Oroboros oxygraph. Data from Fletcher et al. (2019).

Adjusted model to test the model's ability to predict palmitoyl-CoA and octanoyl-CoA dehydrogenation in human liver lysate, with and without anti-MCAD and anti-VLCAD antibodies. Data from Aoyama et al. (1995).

Human mitochondrial fatty acid oxidation of saturated, even chain acyl-Coas beginning at C16. See Model description for detail.

Unzip model notebooks and keep in the same folder. Notebook names state which notebooks need to be run before them in order for them to word, e.g. "[needs-(1)]" indicates that the notebook numbered 1 must be run and its exported output generated before the given notebook can work. This has to do with the model being generated in only one notebook to avoid duplication.

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.