In this research theme, we use digital information of DNA (genomics), RNA (transcriptomics), proteins (proteomics) and chemical processes (metabolomics) in the cell.
Semantic Systems Biology aims to create an integrated view of bioinformatics resources amenable to computational processes. Resources in the life science are highly distributed and semantically heterogeneous because still most of the information available is presented in natural language only, in the form of scientific publications.
Semantic Web technologies allow us to interconnect data in a way similar to how web pages are interconnected and are the technologies that enable the development of the so-called internet of things. Interconnections of data sources requires that the metadata, the data about the data, is present in a sufficient amount and in the right format. For this minimal information models, controlled ontologies and community accepted formats are used.
Semantic interoperability thus warrants machine-actionability of data sources and semantic integration of machine-actionable bioinformatics resources helps us to develop computational models, support decision making and design strategies for microbial communities and other biological systems.
Methods and approaches
In Semantic Systems Biology we take advantage of the newest developments in information technologies, such as:
- semantic databases
- SPARQL endpoints and federated querying
We combine these with FAIR principles for biological data management and extensive modelling.