The Systems Biology and Evolution Toolbox (SBEToolbox) is an open-source, menu-driven software user interface and function library developed in MATLAB for Biological Network Analysis. It allows researchers to explore complex biological networks, capture structural properties, and process large datasets. The top features of SBEToolbox include: Network Centrality & Topological Metrics
Multiple Centralities: Computes degree, betweenness, closeness, and eigenvector centralities to identify core biological components.
Broker Node Identification: Pinpoints “broker genes” that bridge disparate network clusters, which are often linked to disease states.
Network Statistics: Calculates global and local topological parameters to evaluate overall network structure. Advanced Clustering Algorithms
MCL (Markov Clustering): Segregates large networks into distinct biological modules based on random walks.
mCode (Molecular Complex Detection): Detects densely connected regions to locate protein complexes.
ClusterONE: Identifies overlapping protein complexes and functional modules within noisy high-throughput data. Graph Generation & Visualization
Random Network Models: Generates artificial biological baselines like small-world networks and ring lattices.
Layout Algorithms: Visualizes biological pathways using a variety of embedded graph layout frameworks.
Publication-Quality Plots: Utilizes MATLAB’s rendering engine to export customizable, high-resolution figures. Flexible Data Handling & Extensibility
Workspace Exporting: Saves computation results directly to the MATLAB workspace, file system, or clipboard.
Format Conversions: Converts and parses standard biological network file formats seamlessly.
Plugin Architecture: Features high-level functions that let developers easily write and tailor custom analytical plugins.
(Note: If your work focuses more on dynamic biochemical simulation or SBML file modeling, you might also look into the Systems Biology Toolbox 2 (SBTOOLBOX2), which is a separate MATLAB package tailored for kinetic modeling rather than pure graph network analysis).
If you are setting this up, what type of biological data (e.g., protein-protein interactions, gene regulatory networks) are you looking to analyze? I can guide you on which clustering algorithm or centrality metric fits your data best.
SBEToolbox: A Matlab Toolbox for Biological Network Analysis