Our research covers topics of practical computer science and life science informatics. Many of the developed algorithms and methods are also implemented in our open source software.

Research Methodologies

Network Analysis and Visualisation

At any moment in time, we are driven by and are an integral part of many interconnected, dynamically changing networks. Networks in the life sciences can represent a diversity of information such as metabolism, gene regulation, neuronal connections, spread of infections, and food webs. These networks can be assigned to different (interconnected) levels, may change over time and usually have additional data related to their elements (nodes and edges). We research algorithms and methods for investigating network properties as well as for network visualisation and exploration in order to understand the shape, central elements, motifs etc. of networks.

Immersive Analytics

We are living in a complex, three-dimensional environment which makes use of  all our senses. Immersive analytics investigates how new interaction and display technologies can be used to support a deep cognitive, perceptual, and/or emotional involvement when understanding and reasoning about data. It focuses on multi-sensory interfaces for collaboration and for facilitating the immersion of users in their data, aiming at a real life experience. We do both basic research in immersive analytics (such as granularity of immersion, task performance, collaboration in immersive environments) as well as the development of immersive analytics applications in the life sciences. See also here for more information.

Modelling and Simulation

Life is a dynamic process. Computational modeling helps in understanding biological processes (from processes in organisms to whole ecosystems) as well as in hypothesis generation (such as for drug discovery). We research a range of topics related to modeling and simulation in the life sciences from information systems for multilevel representation of metabolic pathways, models and experimental data; to modelling, simulation, and evaluation of cellular processes (from metabolism to cellular membranes to cells); to visualisation and immersive analytics methods for exploring data, simulation and analysis results.

Knowledge Representation and Data Integration

Information and knowledge in the life sciences is often distributed and unstructured, and experimental methods result in diverse data sets about the same biological object. Ontologies, standards and methods for their analysis and exploration help in better knowledge representation and reasoning and provide a basis for better data integration. We are part of larger consortia for standard and ontology development and we conduct research on algorithms and methods for working with standards in the life sciences (such as verification and translation of standards and their usage for knowledge discovery).