Metaboverse

Metaboverse enables automated exploration and contextualization of metabolic and omic data within complex biological networks to reveal regulatory patterns and relationships.

Key Features:

• Multi-omic integration: Integrates multi-omic and single-omic datasets onto dynamic representations of metabolic pathways and networks.
• Real-time pattern extraction: Implements algorithms for real-time detection and extraction of regulatory patterns and trends across metabolic networks.
• Missing-data mitigation: Applies methods to mitigate the impact of missing measurements, enabling pattern recognition across multiple reactions despite incomplete datasets.
• Automated contextualization: Automates contextualization of metabolic measurements within complex network topology to identify relationships beyond individual reactions.

Scientific Applications:

• Detection of indirect perturbation effects: Identifies indirect effects of perturbations that propagate across canonical pathways and may be missed by pathway-level analyses.
• Granular pathway resolution: Provides resolution beyond traditional pathway-level analysis to contextualize effects of perturbations regardless of magnitude.
• Clinical signature discovery: Has been used to uncover multi-dimensional signatures correlated with clinical outcomes such as survival in lung adenocarcinoma.
• Metabolic regulatory insight: Identified novel regulatory patterns suggesting compensatory roles for specific metabolites, such as citrate during mitochondrial dysfunction.

Methodology:

Uses algorithms for real-time detection and extraction of regulatory patterns and trends, layers omic datasets onto dynamic metabolic network models, and applies methods to mitigate missing measurements to enable pattern recognition across multiple reactions.