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Towards rule-based metabolic databases: a requirement analysis based on KEGG.

Stephan Richter, Ingo Fetzer, Martin Thullner

International Journal of Data Mining and Bioinformatics

|November 10, 2015

Summary

This study identifies inconsistencies in metabolic databases like KEGG and proposes a rule-based approach for accurate metabolic network modeling, enhancing data reliability.

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Metabolic databases are crucial for understanding biological processes.
Automatic construction of metabolic networks requires high data accuracy and consistency.
Existing databases like KEGG present challenges in accuracy and consistency.

Purpose of the Study:

To evaluate accuracy and consistency issues in metabolic databases.
To propose design principles for improving metabolic network modeling.
To present a computational approach for classifying inconsistencies.

Main Methods:

Developed a computational method to classify inconsistencies in metabolic databases.
Analyzed the KEGG database as a case study.
Proposed a rule-based database approach for parameterized molecular species and reactions.

Main Results:

Identified inconsistencies in both substance and reaction entries within KEGG.
Demonstrated a rule-based approach for representing metabolic networks.
Case studies confirmed the applicability and scalability of the proposed method.

Conclusions:

Metabolic databases contain significant accuracy and consistency problems.
A rule-based database approach offers a scalable solution for accurate metabolic network construction.
The proposed design principles can improve the reliability of metabolic network models.