GeneRegulatoryNetworkEngine

Boolean GRN Inference and Attractor Analysis

A pure-Python pipeline for gene regulatory network inference using mutual information and Boolean network attractor analysis.

Features

• GRN inference from expression data (mutual information, ARACNE-style)
• Boolean network construction (binarization + rule inference)
• Attractor identification (synchronous update, state space search)
• Perturbation analysis (single-node knockouts)
• Network motif detection (feed-forward loops, feedback loops)

Results

• 200 samples × 50 TFs + 200 target genes
• Total network edges: 787 (TF-TF: 450, TF-target: 337)
• Max TF out-degree: 23 (TF35)
• Attractors found: 405
• FFLs: 3600, Feedback loops: 225

Usage

pip install numpy scipy matplotlib
python gene_regulatory_network_engine.py

Tags

gene-regulatory-network boolean-network attractor network-inference grn mutual-information