Dynamical Systems


biological processes = complex dynamical systems

Our research team develops methods for data-driven dynamical modeling, which allow for model-based integration of different datasets, the critical assessment of the available information, the comparison of competing biological hypothesis and the tailored selection of future experiments. Following the philosophy of Galileo Galilei:

“Measure what can be measured, and make measurable what cannot be measured.”

we use models to reconstruct properties of the system which cannot be assessed experimentally.

Methodologically, we focus on

  • quantitative dynamical modeling, using
    • ordinary differential equations
    • partial differential equations
    • chemical master equations
    • agent-based models
  • parameter estimation
  • identifiability and uncertainty analysis using, e.g.,
    • profile likelihoods / posteriors
    • Markov chain Monte Carlo sampling
    • model selection / hypothesis testing
    • experimental planning

Thereby, we closely collaborate with other research teams and research groups in the Institute of Computational Biology as well as external groups. Our methods and tools are currently applied in a variety of projects with collaboration partners working in stem cell biology, immunology, neurobiology, development and on medical applications (see -> Projects). Beyond these projects we provide support for the statistical data analysis and data-driven modeling.