Heinz

Heinz identifies conserved active subnetwork modules between two species by integrating differential high-throughput molecular profile measurements with species-specific interaction networks to enable translation of model-organism findings to human biology.

Key Features:

• Conservation-Based Module Discovery: Identifies sets of genes that form connected subnetworks within species-specific interaction networks that exhibit overall differential behavior and contain orthologous genes between the two species being compared.
• Flexible Notion of Conservation: Employs a flexible approach to conservation to improve the biological interpretability and relevance of discovered modules.
• Optimal or Near-Optimal Module Identification: Utilizes an algorithm capable of finding provably optimal or near-optimal conserved active modules.
• Focus on Differential High-Throughput Measurements: Operates on differential high-throughput molecular profile measurements to detect active subnetworks.
• Application to Th17 Differentiation: Has been applied to study mechanisms underlying interleukin-17 producing helper T cells (Th17) differentiation in mouse and human models.

Scientific Applications:

• Hypothesis Generation and Biomarker Discovery: Generates novel hypotheses about cellular processes and derives biomarkers for classification and subtyping from conserved active modules.
• Cross-Species Translation: Facilitates translation of findings from model organisms to human biology by highlighting conserved regulatory modules.
• Immunology and Autoimmunity Research: Supports investigation of immune responses and autoimmune disease mechanisms through conserved module analysis.
• Comparative Analysis of Th17 Differentiation: Enables comparative studies of Th17 differentiation mechanisms between mouse and human models.

Methodology:

Heinz uses a mathematical model that integrates network analysis with differential expression data to identify biologically meaningful conserved subnetworks.