pydruglogics 0.1.10

Constructing, optimizing Boolean Models and performing in-silico perturbations.

PyDrugLogics

Overview

PyDrugLogics is a Python package designed for constructing, optimizing Boolean Models and performs in-silico perturbations of the models.

Core Features

• Construct Boolean model from .sif file
• Load Boolean model from .bnet file
• Optimize Boolean model
• Generate perturbed models
• Evaluate drug synergies

Installation

PyDrugLogics can be installed via PyPi, Conda, or directly from the source.

Install PyDrugLogics from PyPI

The process involves two steps to install the PyDrugLogics core package and its necessary external dependencies.

1. Install PyDrugLogics via pip

pip install pydruglogics

2. Install External Dependency

pip install -r https://raw.githubusercontent.com/druglogics/pydruglogics/main/requirements.txt

This will install the PyDrugLogics package and handle all dependencies automatically.

Install PyDrugLogics via conda

conda install szlaura::pydruglogics

Install from Source

For the latest development version, you can clone the repository and install directly from the source:

git clone https://github.com/druglogics/pydruglogics.git
cd pydruglogics
pip install .
pip install -r requirements.txt

CoLoMoTo Notebook environment

PyDrugLogics is available in the CoLoMoTo Docker and Notebook starting from version 2025-01-01.

Setup CoLoMoTo Docker and Notebook

1. Install the helper script in a terminal:

    pip install -U colomoto-docker

2. Start the CoLoMoTo Notebook (a specific tag can also be given):

    colomoto-docker    # or colomoto-docker -V 2025-01-01

3. Open the Jupiter Notebook and navigate to the tutorials folder to find the PyDrugLogics folder hosting the pydruglogics tutorial notebook.

See more about the CoLoMoTo Docker and Notebook in the documentation.

Testing

1. To run all tests and check code coverage, you need to install test dependencies:

    pip install -r requirements.txt
    pip install -e .[test]

2. Then, from the repository root, run:

    pytest tests

You should see a coverage report at the end.

Documentation

For full PyDrugLogics documentation, visit the GitHub Documentation.

Quick Start Guide

Here's a simple example to get you started:

from pydruglogics.model.BooleanModel import BooleanModel
from pydruglogics.input.TrainingData import TrainingData
from pydruglogics.input.Perturbations import Perturbation
from pydruglogics.input.ModelOutputs import ModelOutputs
from pydruglogics.execution.Executor import execute

# Initialize train and predict
model_outputs_dict = {
        "RSK_f": 1.0,
        "MYC": 1.0,
        "TCF7_f": 1.0
    }
model_outputs = ModelOutputs(input_dictionary=model_outputs_dict)

observations = [(["CASP3:0", "CASP8:0","CASP9:0","FOXO_f:0","RSK_f:1","CCND1:1"], 1.0)]
training_data = TrainingData(observations=observations)


drug_data = [['PI', 'PIK3CA', 'inhibits'],
            ['PD', 'MEK_f', 'activates'],
            ['CT','GSK3_f']]
perturbations = Perturbation(drug_data=drug_data)


boolean_model = BooleanModel(file='./ags_cascade_1.0/network.bnet', model_name='test', mutation_type='topology',
                                  attractor_tool='mpbn', attractor_type='trapspaces')

observed_synergy_scores = ["PI-PD", "PI-5Z", "PD-AK", "AK-5Z"]


ga_args = {
        'num_generations': 20,
        'num_parents_mating': 3,
        'mutation_num_genes': 3,
        'fitness_batch_size': 20
}

ev_args = {
        'num_best_solutions': 3,
        'num_of_runs': 30,
        'num_of_cores': 4
}


train_params = {
        'boolean_model': boolean_model,
        'model_outputs': model_outputs,
        'training_data': training_data,
        'ga_args': ga_args,
        'ev_args': ev_args
}

predict_params = {
        'perturbations': perturbations,
        'model_outputs': model_outputs,
        'observed_synergy_scores': observed_synergy_scores,
        'synergy_method': 'bliss'
}

# run train and predict
execute(train_params=train_params, predict_params=predict_params)

For a more detailed tutorial, please visit the documentation or the tutorial.

Contributing to PyDrugLogics

We welcome contributions to PyDrugLogics!

How to Contribute

1. Fork and clone the repository:

   git clone https://github.com/druglogics/pydruglogics.git
   cd pydruglogics
   

2. Create a feature branch:

   git checkout -b my-feature-branch
   

3. Make changes and write tests for new features or bug fixes.
4. Run tests to ensure everything works:

   pytest tests
   

5. Commit and push your changes:

   git commit -m "Describe your changes"
   git push origin my-feature-branch
   

6. Open a pull request against the main repository.

Guidelines

• Follow PEP8 code style.
• Write clear commit messages.
• Update documentation if adding new functionality.
• Ensure all tests pass before submitting a PR.
• For new features or significant changes, we recommend opening an issue or discussing with the maintainer @szlaura first.

Reporting Issues

If you encounter a bug or wish to request a feature, please report it on our GitHub issue tracker:

GitHub Issues Page

When reporting an issue, include:

• Your operating system and version (e.g., Ubuntu 22.04)
• Python version (e.g., Python 3.11.10)
• The error message and traceback (if applicable)
• Steps to reproduce the issue

Thank you for your help!

Citing PyDrugLogics

If you use PyDrugLogics, please cite the paper:

Szekeres, L., Zobolas, J. (2025): PyDrugLogics: A Python Package for Predicting Drug Synergies Using Boolean Models. DOI: 10.21105/joss.08038

@article{Szekeres2025,
  doi = {10.21105/joss.08038},
  url = {https://doi.org/10.21105/joss.08038},
  year = {2025},
  publisher = {The Open Journal},
  volume = {10},
  number = {114},
  pages = {8038},
  author = {Szekeres, Laura and Zobolas, John},
  title = {PyDrugLogics: A Python Package for Predicting Drug Synergies Using Boolean Models},
  journal = {Journal of Open Source Software}
}