CANA v1.0.0: efficient quantification of canalization in automata networks

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Biomolecular networks exhibit canalization, a crucial mechanism for maintaining cellular function amidst environmental noise.
• Functional equivalence of biomolecular regulators is a potential, yet understudied, contributor to canalization.

Purpose of the Study:

• Introduce CANA v1.0.0, an open-source Python package for analyzing canalization in automata network models.
• Present and integrate 'schematodes,' a novel exact method for identifying maximal symmetry groups in discrete functions.
• Investigate the distribution of symmetry in experimentally-supported biological networks from the Cell Collective (CC) repository.

Main Methods:

• Developed and integrated the 'schematodes' exact method for symmetry group identification into CANA.
• Utilized automata network models, a type of discrete dynamical system, to represent biomolecular networks.
• Applied CANA v1.0.0 to analyze symmetry in 74 models from the Cell Collective database and compared findings to random network models.

Main Results:

• The 'schematodes' method significantly outperforms previous inexact methods in both speed and accuracy for symmetry detection.
• The distribution of symmetry in the Cell Collective networks is statistically distinct from random networks with similar connectivity and bias (p≪0.001).
• Cell Collective networks show a wider spread of symmetry compared to null models, indicating enrichment in functions with extreme symmetry or asymmetry.

Conclusions:

• CANA v1.0.0 provides a robust platform for studying canalization through symmetry analysis in discrete dynamical systems.
• The Cell Collective repository exhibits a non-random distribution of symmetry, suggesting biological relevance.
• The findings highlight the importance of exploring symmetry as a mechanism for canalization in biological systems.