Data-driven Computational Modelling


 The junior research group “Data-driven Computational Modeling” focuses on the development of mathematical and computational methods for the data-driven analysis of coupled, multi-scale biological processes. These methods facilitate the 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. This results in an improved holistic understanding of biological systems

Methodologically, our research focuses on:

  • Mechanistic modeling using
    • ordinary differential equations,
    • partial differential equations,
    • chemical master equations,
    • mixed-effect models and
    • agent-based models
  • Statistical analysis using mixture models
  • Parameter estimation
  • Identifiability and uncertainty analysis using
    • profile likelihoods / posteriors and
    • Markov chain Monte Carlo sampling
  • Model selection and hypothesis testing
  • Experimental planning

In more than 20 national and international collaborations with biochemists, biologists, engineers and physicists, we successfully employed statistical and mechanistic model to addressed research questions in the fields of molecular biology, microbiology, stem cell biology, immunology and developmental biology. In addition, we collaborate with clinicians to improve the understanding of leukemia and stomach cancer and to develop predictive markers for targeted treatment. In the different interdisciplinary projects we studied signal transduction using data from eukaryotic microorganisms, zebrafish, human cell lines, primary cells (patient-derived Xenograph) mouse models and patients. A biological focus of our research is the study of cell-cell variability and patient-patient variability along with its influence on decision making. 

An overview about ongoing project is provided in the figure below and for details we refer to list of ongoing projects.

Source: HMGU