SAGPAR

SAGPAR reconstructs metabolic pathways from structural representations of metabolites by integrating thermodynamic thresholds and structural properties to generate energetically feasible pathway predictions for metabolic engineering and systems biology.

Key Features:

• Automated Pathway Reconstruction: Automates generation of metabolic pathways from structural representations of metabolites using user-specified modeling features.
• Thermodynamic Considerations: Incorporates thermodynamic thresholds to ensure reconstructed pathways are energetically feasible.
• Structural Properties Integration: Considers structural properties of metabolites to increase precision and biological relevance of reconstructed pathways.
• Comparative Performance: Demonstrated superior speed and robustness in comparative studies against Copasi, PHT, Gepasi, Jarnac, and Path-A.

Scientific Applications:

• Systems Biology: Supports analysis of metabolic network interactions and dynamics by providing structurally and thermodynamically constrained pathway reconstructions.
• Metabolic Engineering: Enables prediction and design of pathway outcomes to guide metabolic engineering and optimization efforts.
• Pathway Prediction: Facilitates prediction of feasible biochemical routes under varying conditions using structural and thermodynamic constraints.
• Experimental Design and Optimization: Aids design of experiments and optimization of metabolic processes by supplying candidate pathways aligned with energetic feasibility.

Methodology:

SAGPAR leverages structural representations of metabolites and user-specified modeling features to automatically reconstruct pathways while applying thermodynamic thresholds and structural constraints; validation was performed on standard pathway datasets from Mycoplasma pneumoniae M129 (25 pathways) and Homo sapiens (24 pathways) sourced from KEGG and PubChem Compound.