AstroLogics: A simulation-based framework for the analysis of Boolean model ensembles

This is a repository of data, code and analyses of AstroLogics framework. A step-by step tutorial can be found in the folder tutorial. Please have a look at our tutorials.

Overview

AstroLogics is a Python package designed for analysing Boolean model ensemble, a product of Boolean model synthesis from method such as Bonesis.

Our framework includes two major processes

1. Dynamical properties analysis :
   • Calculate distance between models through probabilistic approximation via MaBoSS.
2. Logical function evaluation :
   • Feature logical equations and identify key logical features between model clusters
3. Statistical analysis :
   • Perform statistical analysis between model clusters to identify key logical features between clusters

Overview of the framework showing the two major processes in the framework. Dynamics: dynamical properties analysis. Logics: Logical function evaluation Statistics: statistical framework to link model's logic with statistics.

Getting Started

Requirements (for AstroLogics)

• Python version 3.8 or greater
• Python's packages listed here:
  • pandas
  • numpy
  • scipy, sklearn
  • maboss
  • boolsim
  • bonesis
  • mpbn

Installation

There are several ways to install AstroLogics

PyPi

pip install astrologics

Conda

conda install -c colomoto astrologics

From source

First clone this directory:

git clone https://github.com/sysbio-curie/AstroLogics

Then install AstroLogics with pip

pip install AstroLogics

Tutorials

Tutorials are available as Jupyter notebooks

Run with Binder

Run locally with Docker

To run this notebook using the built docker image, run :

docker run -p 8888:8888 -d sysbiocurie/astrologics

Run locally with Conda

Creating the conda environment

conda env create --file environment.yml

To activate it :

conda activate astrologics

To run the notebook:

jupyter-lab

Documentation

Our documentation is available on ReadTheDocs

Citing AstroLogics

To cite AstroLogics work, please refer to: https://doi.org/10.1093/bioinformatics/btag555