BooN

The BooN project provides a complete set of functionalities for Boolean Network (BooN) analysis. It was originally designed to explore the modeling of genetic networks by Boolean networks. The documentation related to the functions can be found here: https://franck-delaplace.github.io/BooN/. To compute BooN using the Graphical Interface, please run boonify. The project includes:

• the definition of a Boolean network with the possibility to load and save it;
• the computation of the model of dynamics with respect to a mode policy;
• the definition of the interaction graph including a modular decomposition of the interaction;
• the computation of equilibria based on a dynamics model;
• the efficient symbolic computation of stable states based on SAT solver;
• the controllability analysis predicting which variables must be frozen to reach the expected goal at stable states based on the possibility and necessity query;
• also, different basic functionalities are included as: update formula and importing/exporting to a text file the Boolean network.

BooN structure

The BooN project comprises 3 modules:

• boon module is related to the manipulation of Boolean network named BooN, which is an object.
• logic module includes basic functions on propositional formula, as well as more advanced features like fast CNF conversion for large formulas, CNF conversion using Tseitin's method, and prime implicant calculation. These functions are used in the BooN modules.
• The boonify module is the graphical interface manipulating BooN: computation of a dynamical model for synchronous and asynchronous mode, the computation ot the stable states, and the controllability analysis. For exploring BooN interactively run boonify.py

Introductory example

example.ipynb is a Jupyter code illustrating the major functionalities of BooN. It does not include the controllability which is detailed in the real case study.

Real case study

A real case study on breast cancer is available in the Wiki at Section Breast cancer case study (https://github.com/Franck-Delaplace/BooN/wiki/Case-Study-:-Breast-Cancer-cause-&-drug-target-inference). This case study describes step-by-step the process for elucidating the cause of cancer and discovering drug targets using the graphical user interface.

BooN installation

Go in the directory of BooN and type:

• pip install .
• or, python -m pip install .

To cite this work

If you wish to cite this work, please use the following citation:
Causal Reasoning on Boolean Control Networks Based on Abduction: Theory and Application to Cancer Drug Discovery
Celia Biane, Franck Delaplace
IEEE/ACM Trans Computer Biology & Bioinformatics . 2019 Sep-Oct;16(5):1574-1585.
PMID: 30582550 - DOI: 10.1109/TCBB.2018.2889102