Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1504/IJDMB.2015.072103
Titel (primär) Towards rule-based metabolic databases: a requirement analysis based on KEGG
Autor Richter, S.; Fetzer, I.; Thullner, M.; Centler, F.; Dittrich, P.
Journal / Serie International Journal of Data Mining and Bioinformatics
Erscheinungsjahr 2015
Department UMB
Band/Volume 13
Heft 3
Seite von 289
Seite bis 319
Sprache englisch
Keywords metabolism; rule-based metabolic databases; rule-based modelling; inconsistency check; cell models; KEGG database; reaction balance; pathway analysis; ontology; bioinformatics; classification; inconsistencies; parameterised molecular species; parameterised reactions
UFZ Querschnittsthemen RU4;
Abstract Knowledge of metabolic processes is collected in easily accessible online databases which are increasing rapidly in content and detail. Using these databases for the automatic construction of metabolic network models requires high accuracy and consistency. In this bipartite study we evaluate current accuracy and consistency problems using the KEGG database as a prominent example and propose design principles for dealing with such problems. In the first half, we present our computational approach for classifying inconsistencies and provide an overview of the classes of inconsistencies we identified. We detected inconsistencies both for database entries referring to substances and entries referring to reactions. In the second part, we present strategies to deal with the detected problem classes. We especially propose a rule-based database approach which allows for the inclusion of parameterised molecular species and parameterised reactions. Detailed case-studies and a comparison of explicit networks from KEGG with their anticipated rule-based representation underline the applicability and scalability of this approach.
dauerhafte UFZ-Verlinkung
Richter, S., Fetzer, I., Thullner, M., Centler, F., Dittrich, P. (2015):
Towards rule-based metabolic databases: a requirement analysis based on KEGG
Int. J. Data Min. Bioinform. 13 (3), 289 - 319