AstroLogics: a simulation-based framework for the analysis of boolean model ensembles

Boolean networks (BNs) have emerged as versatile tools for modeling cellular regulatory mechanisms due to their ability to capture key biological features despite their simplicity. Multiple BN synthesis methods have emerged in recent decades, aiming to infer BNs with dynamics that correspond to experimental data. Often, these methods generate multiple BN candidates or "model ensembles". While these ensembles are valuable for representing cell populations and their heterogeneity, they are typically treated as a single component without examining their constituent features.

We present AstroLogics, a novel framework designed to analyze and identify differences in both dynamical behavior and logical regulation within a BN model ensemble. The framework calculates dynamical distances between BNs through exploration of their state transition graphs (STGs), enabling clustering of similarly functioning models that may represent different cellular fates or signaling mechanisms. AstroLogics also identifies key logical properties that govern each cluster, highlighting the core regulatory structures that differentiate model behaviors. Our approach leverages MaBoSS, a stochastic simulation tool that implements the Boolean Kinetic Monte-Carlo algorithm to address time interpretation in BNs. This probabilistic estimation method allows efficient probing of BN dynamics through stochastic simulations, overcoming the computational limitations of exhaustive STG analysis. Our framework also provides powerful visualization and classification of the BN ensemble. Through multiple use cases, we demonstrate how AstroLogics facilitates comprehensive analyses of model diversity and discovery of key regulatory structures within a BN ensemble.

The AstroLogics package, along with tutorials and datasets, are available at https://github.com/sysbio-curie/AstroLogics.