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A Web Tool for Generating High Quality Machine-readable Biological Pathways

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Published on: February 8, 2017

Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across

Huan Jin¹, Joshua M Mitchell^2,3,4, Hunter N B Moseley^2,3,4,5

¹Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA.

|September 16, 2020

Summary

A new graph coloring method creates unique atom identifiers for metabolic networks, improving compound harmonization and database accuracy for metabolic flux analysis.

Keywords:

atom identifier atom-resolved metabolic network common subgraph isomorphism compound identifier database harmonization graph theory metabolomics

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Area of Science:

Metabolic Engineering
Bioinformatics
Systems Biology

Background:

Metabolic flux analysis relies on accurate metabolic models and profiles.
High-quality metabolomics data is now available, but complete atom-resolved metabolic networks are often lacking.
This hinders the development of reliable metabolic models for various organisms.

Purpose of the Study:

To develop a novel method for constructing atom-resolved metabolic networks.
To enable automatic identification of molecular symmetry and compound harmonization across databases.
To improve the accuracy and integration of metabolic network data.

Main Methods:

Developed a neighborhood-specific graph coloring method to assign unique identifiers to each atom.
Generated compound coloring identifiers for harmonizing compounds across databases.
Utilized Enzyme Commission (EC) numbers to validate reaction correspondences.

Main Results:

Successfully created atom-resolved metabolic networks.
Detected 8865 correspondences between KEGG and MetaCyc compounds, with 5451 confirmed.
Validated 1848 additional correspondences using reaction EC numbers.
Identified errors in compound representation within KEGG and MetaCyc.

Conclusions:

The graph coloring method facilitates the construction of atom-resolved metabolic networks.
Compound coloring identifiers enable effective harmonization and integration of metabolic databases.
This methodology aids in database curation and improves metabolic network analysis.