BioNet-Mining

BioNet-Mining identifies frequent structural patterns in biochemical reaction networks encoded in the Systems Biology Markup Language (SBML) to analyze recurring network motifs across computational models.

Key Features:

• Pattern Identification: Employs subgraph mining algorithms to detect frequent structural patterns within biochemical reaction networks encoded in SBML.
• Graphical Representation: Converts textual descriptions of identified patterns into graphical representations.
• Pattern Distribution Analysis: Analyzes the distribution of identified patterns across a selected set of models.
• Ontology Integration: Incorporates terms from the Systems Biology Ontology to annotate identified patterns.
• Customizability: Operates on user-supplied model sets or on models stored in the MaSyMoS graph database.

Scientific Applications:

• Understanding Biological Processes: Elucidates recurring motifs and central processes encoded within biochemical networks.
• Evaluating Biological Motifs: Supports evaluation of postulated biological motifs by identifying their structural occurrences across models.
• Model Similarity Analysis: Provides a structural similarity measure for comparative analysis and model refinement based on shared patterns.

Methodology:

Applies subgraph mining techniques to SBML-encoded reaction networks and was validated on 575 models from the curated branch of BioModels.