AstroLogics: A simulation-based analysis framework for monotonous Boolean model ensemble

Abstract

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, which aim to infer BNs with dynamics that corresponding to the experimental data, have emerged in a recent decade. However, these methods often posed a challenge as multiple BN candidates or "model ensemble" can be generated. While these ensembles are valuable for representing cell populations and their heterogeneity, current methods typically treat them as single components 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 BN model ensembles. 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 approximation 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 capabilities for model ensembles. Through multiple use cases, we demonstrate how AstroLogics facilitates comprehensive analysis of model diversity, and discovery of key regulatory structures within BN ensembles.