Boolean Modeling of Parkinson's Disease
This repository contains Python and R scripts for analyzing and modeling Parkinson's disease mechanisms using Boolean modeling and data processing tools. The project uses MaBoSS simulations and additional analytical pipelines to extract insights into Parkinson's disease pathways.
Table of Contents
Overview
This project focuses on Boolean modeling for Parkinson's disease to identify key states and pathways in disease progression. It uses MaBoSS simulations for probabilistic Boolean network modeling and integrates data analysis pipelines written in R and Python.
The repository is hosted at:
Boolean Modeling of Parkinson's Disease GitLab Repository
Setup Instructions
Python Environment
-
Clone the Repository
git clone https://gitlab.lcsb.uni.lu/lcsb-biocore/publications/hemedan23-boolean-modelling-of-pd.git cd publications/hemedan23-boolean-modelling-of-pd
-
Create and Activate a Virtual Environment
- On macOS/Linux:
python3 -m venv .venv source .venv/bin/activate
- On Windows:
python -m venv .venv .venv\Scripts\activate
- On macOS/Linux:
-
Install Dependencies Install the required Python libraries:
pip install -r requirements.txt
R Environment
-
Install
renv
Make sure you haverenv
installed in R. If not, install it:install.packages("renv")
-
Restore R Environment Run the following in the R console to restore the package environment:
renv::restore()
Scripts and Usage
Python Script
Script: MBSS_FormatTable.py
This script processes MaBoSS simulation outputs to generate probability tables and state distributions.
Dependencies
maboss
pandas
Usage
Run the Python script as follows:
python MBSS_FormatTable.py <file.bnd> <file.cfg> [<optional_threshold>] [-mb <maboss_executable>]
Parameters:
-
<file.bnd>
: Boundary condition file. -
<file.cfg>
: Configuration file. -
<optional_threshold>
: Probability threshold (optional). -
-mb <maboss_executable>
: Path to the MaBoSS executable (optional).
Example:
python MBSS_FormatTable.py example.bnd example.cfg 0.01 -mb /path/to/maboss
R Scripts
parsingTraj.R
1. This script extracts probability trajectories from MaBoSS .probtraj
files.
Dependencies
optparse
data.table
Usage Run the script with required arguments:
Rscript parsingTraj.R -i <input_file> -o <output_prefix> -n "<conditions>" [options]
Parameters:
-
-i
: Input.probtraj
file. -
-o
: Output file prefix (default:StateProbabilities.txt
). -
-n
: Conditions to extract trajectories for. -
-s
: States for each condition (optional).
Example:
Rscript parsingTraj.R -i input.probtraj -o output -n "phenotype1,phenotype2"
workflowRun.R
2. This script implements workflows for Boolean modeling using provided .bnd
and .cfg
files.
Dependencies
BoolNet
clusterProfiler
pathview
Usage Run the script as follows:
Rscript workflowRun.R -b <boundary_file> -c <config_file> -p <parameter_file>
Parameters:
-
-b
: Boundary file path. -
-c
: Configuration file path. -
-p
: Parameter file path.
Example:
Rscript workflowRun.R -b model.bnd -c config.cfg -p parameters.csv
Dependencies
Python Dependencies
maboss
pandas
Install them with:
pip install -r requirements.txt
R Dependencies
The renv
package is used for managing R dependencies. Run:
renv::restore()
Notes
- Ensure you have the required MaBoSS binaries installed and accessible if using the Python script.
- Use
.gitignore
to avoid committing large or unnecessary files like the.venv
directory or intermediate results.
License
This project is distributed under the MIT License.
Contact
For questions or issues, please contact:
Ahmed Hemedan
Alternatively, open an issue in the GitLab Repository.