PADMet

PADMet centralizes metabolic network information into a graph-based representation to support genome-scale metabolic model (GSMN) reconstruction, analysis, and traceability.

Key Features:

• Graph-Based Data Centralization: Organizes metabolic network components and associated metadata into a cohesive graph-based structure for unified data management.
• Integration with Personalized Pipelines: Supports personalized reconstruction and enhancement pipelines inspired by AuReMe, preserving modifications and workflow details.
• Traceability and Reproducibility: Records relevant information at each step of the modeling process to maintain provenance and reproducibility of GSMN reconstructions.
• Semantic Query and Exploration: Enables semantic querying of models and metadata through RDF databases to facilitate structured searches and data linkage.
• Application to Non-Model Organisms: Handles metabolic reconstructions for non-model organisms, including extremophile bacteria and eukaryotic algae, preserving species-specific model information.

Scientific Applications:

• Comparative GSMN Analysis: Supports detailed analysis and comparison of genome-scale metabolic models across species to investigate conserved and divergent pathways.
• Reconstruction of Non-Model Species: Facilitates reconstruction and study of metabolic networks in extremophile bacteria and eukaryotic algae.
• Study of Growth Mechanisms and Biosynthetic Pathways: Enables investigation of growth mechanisms and biosynthetic pathways as demonstrated in studies of brown algae such as Saccharina japonica and Cladosiphon okamuranus.
• Hypothesis Generation on Unique Metabolisms: Supports formulation of hypotheses about distinct metabolic processes, for example the divergent abscisic acid biosynthesis pathway in brown algae versus land plants.
• Integration with Metabolic Profiling and Literature: Facilitates combining metabolic profiling data with literature-derived information to improve functional interpretation of networks.

Methodology:

Represents metabolic data as a graph-based structure, provides import/update/analyze/export operations within a library framework, records metadata at each reconstruction step, supports personalized pipelines (AuReMe-like), and enables semantic queries via RDF databases.