# Path-based quantification of activation and repression in Boolean models using BooLEVARD

## TL;DR

BooLEVARD is a Python tool that improves the analysis of Boolean models by quantifying signal paths in biological systems, helping understand disease processes and drug effects.

## Contribution

Introduces BooLEVARD, a novel Python package for efficient path-based quantification of activation and repression in Boolean models.

## Key findings

- BooLEVARD accurately identifies non-redundant paths influencing Boolean outcomes in signaling networks.
- The tool was successfully applied to study cancer metastasis and scale across large Boolean models.
- BooLEVARD enhances the representation of signal strength and critical signaling events in biological systems.

## Abstract

Boolean models are a powerful resource for studying dynamic processes of biological systems. However, their inherent discrete nature limits their ability to capture continuous aspects of signal transduction, such as signal strength or protein activation levels. Although existing tools provide some path exploration capabilities that can be used to explore signal transduction circuits, the computational workload often requires simplifying assumptions that compromise the accuracy of the analysis. Here, we introduce BooLEVARD, a Python package designed to efficiently quantify the number of paths leading either to node activation or repression in Boolean models, which offers a more detailed and quantitative perspective on how molecular signals propagate through signaling networks. By focusing on the collection of non-redundant paths directly influencing Boolean outcomes, BooLEVARD enhances the precision of signal strength representation. We showcase the application of BooLEVARD in studying cell-fate decisions using a Boolean model of cancer metastasis, demonstrating its ability to identify critical signaling events. In addition, through a second use case, we demonstrated BooLEVARD’s capability to scale efficiently across increasingly large and connected Boolean models. Through these properties, BooLEVARD provides a distinctive tool for quantitative analysis of signaling dynamics within Boolean models, which can increase our understanding of disease development and drug responses. BooLEVARD is freely available at https://github.com/farinasm/boolevard.