Network Motif Clustering Toolbox

Network Motif Clustering Toolbox clusters topological network motifs to analyze motif aggregation and identify functionally coherent modules within integrated biological networks, implemented for Matlab/Octave.

Key Features:

• Clustering Topological Network Motifs: Clusters small, recurring topological motifs that represent functional relationships in networks.
• Statistical Significance Assessment: Implements a method for assessing the statistical significance of motif aggregation within networks.
• Identification of Overlapping Motif Clusters: Detects clusters in which network motifs overlap to reveal interacting motif structures.
• Integrated Network Analysis: Analyzes integrated networks comprising transcriptional, posttranslational, and protein-protein interactions, with application to yeast data.
• Matlab/Octave Implementation: Provided as an implementation for Matlab and Octave environments.

Scientific Applications:

• Functional Module Identification: Identifies self-contained modules by clustering enriched motifs that aggregate into coherent units.
• Exploration of Network Evolution: Supports investigation of theoretical models such as duplication-divergence mechanisms and hierarchical scale-free networks and examines relationships between motif aggregation and evolutionary models.
• Network Modularity Analysis: Enables study of how motif aggregation reflects local modularity properties of biological networks.
• Novel Functional Network Themes: Detects clusters of non-enriched yet significantly aggregated motifs to uncover new functional themes within complex networks.

Methodology:

Clustering of topological network motifs, assessment of statistical significance of motif aggregation, identification of overlapping motif clusters, and analysis of an integrated network comprising transcriptional, posttranslational, and protein-protein interactions in yeast, showing motif aggregation as a local modularity property independent of motif enrichment.