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

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Boolean networks (BNs) model cellular regulation, capturing essential biological features with simplicity.
• BN synthesis methods generate model ensembles representing cell populations and heterogeneity.
• Existing methods often treat BN ensembles monolithically, overlooking constituent model differences.

Purpose of the Study:

• To introduce AstroLogics, a novel framework for analyzing BN model ensembles.
• To identify differences in dynamical behavior and logical regulation within BN ensembles.
• To enable comprehensive analysis of model diversity and discovery of key regulatory structures.

Main Methods:

• AstroLogics calculates dynamical distances between BNs by exploring state transition graphs (STGs).
• It employs MaBoSS, a stochastic simulation tool using the Boolean Kinetic Monte-Carlo algorithm.
• The framework provides visualization and classification of BN ensembles.

Main Results:

• AstroLogics enables clustering of similarly functioning BNs, representing different cellular fates.
• It identifies key logical properties governing clusters, highlighting differentiating regulatory structures.
• The framework overcomes computational limitations of exhaustive STG analysis through probabilistic estimation.

Conclusions:

• AstroLogics facilitates in-depth analysis of BN ensemble diversity.
• It aids in discovering critical regulatory structures that drive distinct model behaviors.
• The framework enhances understanding of cellular heterogeneity and signaling mechanisms modeled by BNs.