pydruglogics 0.1.0

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

PyDrugLogics

Overview

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

Installation

PyDrugLogics can be installed via pip, conda, or directly from the source.

Install PyDrugLogics from PyPI

pip install pydruglogics

Install PyDrugLogics via conda

conda install pydruglogics

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

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

Documentation

For full documentation, visit the GitHub Documentation.

Quick Start Guide

Here's a simple example to get you started:

from pydruglogics import BooleanModel, ModelOutputs, TrainingData, Perturbation
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.

CoLoMoTo Docker Integration

Note: This section will be updated when Colomoto Docker integration is completed.

Citing PyDrugLogics

If you use PyDrugLogics, please cite the paper:

Flobak, Å., Zobolas, J. et al. (2023): Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization. DOI: 10.3389/fsysb.2023.1252961

@Article{druglogics2023,
  title = {Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization},
  author = {Flobak, Å., Zobolas, J. and Other Authors},
  journal = {Frontiers},
  year = {2023},
  month = {nov},
  doi = {10.3389/fsysb.2023.1252961},
  url = {https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2023.1252961/full},
}