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

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Boolean networks (BNs) are simplified models of complex cellular regulatory mechanisms.
• BN synthesis methods often produce model ensembles representing cell populations and heterogeneity.
• Analyzing the diversity within these ensembles is crucial for understanding biological systems.

Purpose of the Study:

• To introduce AstroLogics, a novel framework for analyzing Boolean network model ensembles.
• To identify differences in dynamical behavior and logical regulation within BN ensembles.
• To facilitate the discovery of key regulatory structures driving model diversity.

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 enables clustering of similar models and identification of governing logical properties.

Main Results:

• AstroLogics successfully clusters BN models based on dynamical behavior.
• It identifies key logical properties differentiating model clusters.
• The framework provides visualization and classification tools for BN ensembles.
• Use cases demonstrate comprehensive analysis of model diversity and regulatory structure discovery.

Conclusions:

• AstroLogics offers a powerful approach to analyze the heterogeneity of Boolean network model ensembles.
• It bridges the gap between ensemble representation and understanding underlying regulatory logic.
• The framework enhances the utility of BN ensembles for biological discovery.