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Related Experiment Videos

Organizing and computing metabolic pathway data in terms of binary relations

S Goto1, H Bono, H Ogata

  • 1Institute for Chemical Research, Kyoto University, Japan.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 1, 1997
PubMed
Summary

The KEGG database system organizes gene and genome functions using binary relations. This approach aids in understanding metabolic pathways and predicting genomic sequence functions.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Genomic and functional data require sophisticated organization for analysis.
  • Existing databases lack comprehensive integration of molecular interactions and pathways.
  • Understanding gene and genome functions necessitates a structured approach to biological data.

Purpose of the Study:

  • To introduce the KEGG database system for computerizing gene and genome functions.
  • To present a novel representation of metabolic pathway data using binary relations and hierarchical classifications.
  • To demonstrate the utility of KEGG for functional prediction of genomic sequences.

Main Methods:

  • Development of a database system (KEGG) integrating genes, molecules, and pathways.

Related Experiment Videos

  • Utilizing binary relations and hierarchical classifications for metabolic pathway data representation.
  • Defining database operations as extensions of relational operations and path computation as deduction from binary relations.
  • Linking KEGG to existing databases via the DBGET retrieval system.

    • Main Results:

    • KEGG provides a structured framework for metabolic pathway data.
    • The system effectively represents complex biological interactions.
    • Functional prediction of genomic sequences is demonstrated using KEGG.
    • Interconnectivity with existing databases enhances data accessibility.

    Conclusions:

    • KEGG offers a powerful tool for organizing and analyzing functional genomics data.
    • The binary relation model provides a robust method for representing biological pathways.
    • KEGG facilitates functional prediction and enhances understanding of genomic sequences.