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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

  1. 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
  2. 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
  3. Install Dependencies Install the required Python libraries:

    pip install -r requirements.txt

R Environment

  1. Install renv Make sure you have renv installed in R. If not, install it:

    install.packages("renv")
  2. 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

1. parsingTraj.R

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"

2. workflowRun.R

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.