SteinerNet

SteinerNet reconstructs biologically meaningful signaling networks by integrating transcriptional profiling, proteomics, and interactome data to connect experimentally detected proteins and genes.

Key Features:

• Data integration: Integrates transcriptional profiling, proteomics, and interactome data to synthesize complex molecular descriptions into coherent regulatory and signaling pathways.
• Algorithmic approach: Applies the prize-collecting Steiner tree problem to identify optimal network connections among detected proteins and genes.
• Organism interactomes: Uses interactomes from yeast, human, mouse, Drosophila melanogaster, and Caenorhabditis elegans.
• Custom interactomes: Accepts user-provided interactome data for inclusion in network reconstruction.

Scientific Applications:

• Condition-specific pathway reconstruction: Integrates high-throughput data from specific conditions or cellular responses to reconstruct relevant signaling pathways.
• Pathway discovery: Uncovers biologically meaningful pathways connecting experimentally detected proteins and genes that may be obscured in large datasets.
• Regulatory and signaling network elucidation: Aids in elucidating complex regulatory and signaling networks underlying biological processes and disease states.

Methodology:

The core method solves the prize-collecting Steiner tree problem to identify optimal subnetworks connecting experimentally detected proteins and genes within provided interactomes.