Jan Hasenauer
Prof. Dr.-Ing. Jan Hasenauer
  • Life & Medical Sciences Institute (LIMES)
Research topics
  • systems medicine
  • systems biology
  • mathematical modelling
  • machine learning
In the research group we develop and apply computational methods for the analysis of complex biological processes. We focus on methods facilitating 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. We apply these methods to study intracellular signal processing, cell-to-cell variability, tissue organisation and drug response.
Selected publications

Stapor P, Schmiester L, Wierling C, Merkt S, Pathirana D, Lange BMH, Weindl D, Hasenauer J (2022) Mini-batch optimization enables training of ODE models on large-scale datasets. Nat Comm 13:34.

Fröhlich F, Kessler T, Weindl D, Shadrin A, Schmiester L, Hache H, Muradyan A, Schütte M, Lim J-H, Heinig M, Theis FJ, Lehrach H, Wierling C, Lange B, Hasenauer J (2018) Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model. Cell Syst 7:567-579.e6.

Loos C, Moeller K, Fröhlich F, Hucho T, Hasenauer J (2018) A hierarchical, data-driven approach to modeling single-cell populations predicts latent causes of cell-to-cell variability. Cell Syst 6593-603.e13.

Jagiella N, Rickert D, Theis FJ, Hasenauer J (2017) Parallelization and high-performance computing enables automated statistical inference of multiscale models. Cell Syst 4:194-206.

Jan Hasenauer
Prof. Dr.-Ing. Jan Hasenauer
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