DPCT

DPCT identifies protein complexes within dynamic Protein-Protein Interaction (PPI) networks by integrating TAP and GO annotations with gene expression data to generate temporally resolved subnetworks for complex detection.

Key Features:

• Dynamic Network Analysis: Tailors analysis to dynamic PPI networks, accounting for time-dependent changes in cellular interactions.
• TAP and GO–weighted PPI construction: Uses TAP and GO annotations to construct a weighted PPI network that reduces noise in raw PPI data.
• Gene expression–derived dynamic subnetworks: Employs gene expression data to generate context-specific dynamic subnetworks from the weighted PPI network.
• Memetic algorithm for biclustering: Applies a memetic algorithm combining global and local search strategies to bicluster gene expression data.
• Bicluster-based temporal states: Converts each bicluster into a dynamic subnetwork representing a distinct temporal state to enable identification of temporally relevant protein complexes.

Scientific Applications:

• Systems biology: Resolves time-dependent protein complex formation to elucidate dynamic cellular processes and pathways.
• Drug discovery: Identifies temporally specific protein complexes that may serve as context-dependent therapeutic targets.
• Functional genomics: Links gene expression patterns to dynamic PPI architecture to infer functional modules and complex membership over time.

Methodology:

Construct a weighted PPI network using TAP and GO annotations to reduce noise; bicluster gene expression data via a memetic algorithm to derive dynamic subnetworks corresponding to temporal states; detect protein complexes within each dynamic subnetwork.