Pathmodel

Pathmodel infers novel biochemical reactions and metabolite structures within genome-scale metabolic networks to reconstruct incomplete pathways and enable evolutionary analysis of metabolic pathway drift in emerging model organisms.

Key Features:

• Inference of reactions and structures: Infers novel biochemical reactions and metabolite structures within genome-scale metabolic networks.
• Data integration: Integrates genomic and metabolomic data to support inference and hypothesis generation.
• Logical reasoning formalization: Formalizes logical reasoning to abstract and infer molecular transformations from existing biochemical knowledge.
• Evolutionary analysis framework: Provides a framework to identify metabolic pathway drift and analyze evolutionary turnover of pathways.
• Experimental hypothesis generation: Produces inferred reactions and structures that are amenable to experimental validation.
• Application to incomplete annotations: Operates on emerging model organisms with incomplete biochemical knowledge to extend genome-scale reconstructions.

Scientific Applications:

• Red algal metabolism: Applied to Chondrus crispus to infer pathway changes and metabolite structures, including turnover in sterol and mycosporine-like amino acid synthesis pathways.
• Evolutionary studies: Analyzes how phenotypic conservation can occur despite molecular-level pathway changes (metabolic pathway drift).
• Metabolic network reconstruction: Extends and refines genome-scale metabolic networks in organisms lacking complete biochemical annotations.
• Experimental planning: Generates testable biochemical hypotheses for experimental validation of inferred reactions and metabolites.

Methodology:

Integrates genomic and metabolomic data and formalizes logical reasoning to infer molecular transformations, biochemical reactions, and metabolite structures within genome-scale metabolic networks.