ResponseNet

ResponseNet integrates genetic and transcriptomic screening data to identify signaling and regulatory molecular interaction subnetworks underlying human disease pathways.

Key Features:

• Integration of genetic and transcriptomic data: Combines genetic screen hits (putative response regulators) and transcriptomic profiling results to connect regulatory inputs with observed expression changes.
• Minimum-cost flow optimization: Formulates and efficiently solves a minimum-cost flow optimization problem using linear programming to extract sparse, high-probability subnetworks.
• Human interactome support: Provides an extensive human interactome (ResponseNet 2.0) composed of proteins, genes, and micro-RNAs for identification of molecular interaction paths.
• Output analyses and representation: Produces sparse, weighted subnetworks connecting inputs and includes randomization analysis, graphical and textual representations, and gene ontology enrichment analyses.

Scientific Applications:

• Cellular response characterization: Elucidates signaling and regulatory mechanisms underlying complex cellular responses to stimuli by integrating genetic screening and transcriptomic profiling.
• Disease pathway identification: Identifies molecular interaction paths that link putative response regulators to transcriptomic changes relevant to human disease pathways.
• Functional genomics: Aids interpretation of large-scale genetic and transcriptomic datasets to bridge genotype-to-phenotype relationships.

Methodology:

Formulates and solves a minimum-cost flow optimization problem using linear programming to identify high-probability signaling and regulatory subnetworks by leveraging known molecular interactions to link putative response regulators to observed transcriptomic changes.