Semantic Systems Biology

Systems Biology involves integration of huge volumes of heterogeneous data. Processing of this data requires a high degree of interoperability, defined as a shared understanding and  unambiguous exchange of data and knowledge between humans, computational systems and tools. For FAIR data integration and sharing Semantic Systems Biology exploits Semantic Web technologies.

Using Semantic Systems Biology techniques we are able to efficiently align and integrate heterogeneous data from various sources and develop models supporting decision making and design strategies. We combine top-down and bottom-up approaches together with data integration approaches to design subsequent experiment and intervention strategies.

Two main type of research outputs are produced: i) biological applications, addressing biological questions, and ii) technological developments: tools, models and infrastructures required to address  the challenges in i).

Technological developments: FAIR Semantic Web tools

Poncheewin et al., ( 2020) NG-Tax 2.0: A Semantic Framework for High-Throughput Amplicon Analysis.  Frontiers in Genetics 10, 1366

van Dam et al. (2020) The Empusa code generator and its application to GBOL, an extendable ontology for genome annotation.
Scientific data 6 (1), 1-9

van Dam et al. Interoperable genome annotation with GBOL, an extendable infrastructure for functional data mining (GBOL) Preprint available at bioRxiv

Koehorst et al. (2017)  SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles.  Bioinformatics btx767

Benis et al 2016. Building Pathway Graphs from BioPAX Data in R. F1000Research 5

van Heck et al 2016 Efficient reconstruction of predictive consensus metabolic network models PloS Computational Biol.

van Dam, et al . 2015. RDF2Graph a Tool to Recover, Understand and Validate the Ontology of an RDF Resource. Journal of Biomedical Semantics 6: 39.