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.
General news | 2017-12-12
See video from eminar with Professor Rashid Sumaila, one of the world’s most innovative researchers on the future of the oceans
Research news | 2017-11-30
The PECS-II conference showcased place-based research and how it can help us work towards global sustainability in the Anthropocene
Research news | 2017-11-28
How urban greening and civic ecology projects can improve human well-being and restore crucial ecosystem services
Research news | 2017-11-27
What plantain farmers in Costa Rica can teach us about the inconsistent links between access to ecosystem services and well-being
Research news | 2017-11-23
Centre science director well established among world’s most top-cited and influential scientists
Research news | 2017-11-21
Large-scale changes in Arctic marine food web can be expected within 50 years, some good, but in the long run several critical