Ontology-based instance data validation for high-quality curated biological pathways

Abstract

Background: Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.

Results: We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.

Conclusions: A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

Figure 1

Relationship between the CSO terms and the Petri net elements. The CSO term is followed by the Petri net element in parentheses.

Figure 2

Valid connections between Process and Entity via Connector. Legend for icons on the left: blue ovals for Entity, diamond for Process, and lines between Process and Entity for Connector. Each connection shows the type of the connector: (a) no connection; (b) InputProcessBiological for substrate; (c) InputInhibitorBiological for inhibitor; (d) InputAssociationBiological for activator; and (e) OutputProcessBiological for product.

Figure 3

ME UnknownActivation violating cardinality constraint. Legend: A and B represent the original model and the corrected model after validation, respectively. The biological event causing warnings and the modified parts are in red boxes in the images. The same legend is used in Figure 4. From the literature, we found that Ras{active} acts as an activator of the process, not as an inputprocess entity. Then, the connector from Ras{active} to the process is changed to a dashed line with an arrow in B.

Figure 4

ME_Dimerization violating constraint and property constraints. The event needs one inputprocess entity whose stoichiometric coefficient should be equal to 2. We have found that it takes place because of the misassignment of the event term, which is changed into ME_Binding.