For the passion of revealing stories in relational data to address research questions across the sciences, in the complex system group we develop and apply mathematics, algorithms, and visualizations in integrated methods and tools that we make easy to use for other researchers and students.
We combine interaction data and models of dynamical processes on networks to identify essential nodes or substructures for different functions. Since networks often are big and complex in themselves, we also develop tools to simplify and highlight important structures in networks. In our interdisciplinary approach, the models and tools allow us to study natural phenomena in new ways, which raise further questions that require refined models and tools. Ongoing projects that benefit from this tight integration of mathematical modeling and empirical sciences include decoding signaling networks controlling plant stress responses, mapping historical seasonal outbreaks for reliable modeling of the spread of disease, and delimiting bioprovinces for assessing how they respond to past and present climatic changes.