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

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ID: 322559
2026
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Abstract
Abstract Motivation 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 single components without examining their constituent features. Results 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. Availability and Implementation The AstroLogics package, along with tutorials and datasets, is available at https://github.com/sysbio-curie/AstroLogics.
Reference Key
openalex_W7171143549 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Saran Pankaew, Vincent Noël, Loïc Paulevé, Denis Thieffry, Emmanuel Barillot, Laurence Calzone
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag555
URL
https://doi.org/10.1093/bioinformatics/btag555
Keywords Keywords not found

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