Source: HMGU

Dr.-Ing. Jan Hasenauer
Junior Group Leader Data-driven Computational Modeling

Phone: +49 89 3187-2788
Building/Room: 58a / 003


Short CV

I studied Engineering Cybernetics at the University of Stuttgart and the University of Wisconsin, Madison. After my graduation, I started my PhD studies in systems biology for which I received a Ph.D. degree in 2013. A few months later I became team leader at the Institute of Computational Biology at the Helmholtz Zentrum München. Since August 2015, I lead an independent junior research group at the Helmholtz Zentrum München.

My research focuses on the development of methods for the data-driven modeling of biological processes. These methods enable a model-based integration of different datasets, the critical assessment of available information, the comparison of different biological hypotheses and the tailored selection of future experiments. 


  • Modeling of biological processes using
    • ordinary and partial differential equations,
    • Markov jump processes and the Chemical Master Equation, and
    • mixed-effect models
  • Parameter estimation
  • Model selection
  • Identifiability and uncertainty analysis using
    • profile likelihoods and
    • Markov chain Monte Carlo sampling
  • MATLAB programming 

Selected publications

  • Fröhlich F, Thomas P, Kazeroonian A, Theis FJ, Grima R, Hasenauer J. Inference for stochastic chemical kinetics using moment equations and system size expansion. PLoS Computational Biology, 12(7):e1005030, 2016.
  • Hross S, Hasenauer J. Analysis of CFSE time-series data using division-, age- and label-structured population models. Bioinformatics, 2016.
  • Kazeroonian A, Fröhlich F, Raue A, Theis FJ, Hasenauer J. CERENA: ChEmical REaction Network Analyzer - A toolbox for the simulation and analysis of stochastic chemical kinetics. PLoS ONE, 11(1):e0146732, 2016.
  • Hasenauer J, Jagiella N, Hross S, Theis FJ. Data-driven modelling of biological multi-scale processes. Journal of Coupled Systems and Multiscale Dynamics, 3(2):101–121, 2015.
  • Hasenauer J, Hasenauer C, Hucho T, Theis FJ. ODE constrained mixture modelling: A method for unraveling subpopulation structures and dynamics. PLoS Computational Biology, 10(7):e1003686, 2014.  
  • Hasenauer J, Wolf V, Kazeroonian A, Theis FJ. Method of conditional moments (MCM) for the chemical master equation. Journal of Mathematical Biology, 69(3):687-735, 2014.
  • Heinrich S, Geissen EM, Trautmann S, Kamenz J, Widmer C, Drewe P, Knop M, Radde N, Hasenauer J, Hauf S. Determinants for robustness in spindle assembly checkpoint signaling. Nature Cell Biology, 15(11):1328-1339, 2013.
  • Andres C, Hasenauer J, Ahn HS, Joseph EK, Theis FJ, Allgöwer F, Levine JD, Dib-Hajj SD, Waxman SG, Hucho T. Wound healing growth factor, basic FGF, induces Erk1/2 dependent mechanical hyperalgesia. Pain,154(10):2216-2226, 2013.
  • Hasenauer J, Schittler D, Allgöwer F. Analysis and simulation of division- and label-structured population models: A new tool to analyze proliferation assays. Bulletin of Mathematical Biology, 74(11): 2692-2732, 2012.
  • Hasenauer J, Waldherr S, Doszczak M, Radde N, Scheurich P, Allgöwer F. Identification of models of heterogeneous cell populations from population snapshot data. BMC Bioinformatics, 12(125), 2011.

For a comprehensive list and an independent publication summary see Google Scholar or ReseachGate. 


  • MTZ-Award for Medical Systems Biology for an outstanding Ph.D. thesis(2014)
  • Best Student Paper Award at the 9th International Workshop on Computational Biology, Ulm, Germany (2012)
  • Best Student Paper Award at the 8th International Workshop on Computational Biology, Zürich, Switzerland (2011)
  • Kyb-Alumni Award for an outstanding Diploma thesis (2008)
  • Peter Sagirow Award for an excellent Prediploma (2006)


  • Selection for the Postdoctoral Fellowship Program of the Helmholtz Zentrum München. This provides funding for independent postdoctoral research for 3 years. (2014-2017)
  • Scholarship of the German Research Foundation (DFG) for integrated PhD studies abroad at the University of California, Santa Barbara, USA (2011)
  • Conference scholarship of the Federal Ministry of Education and Research (2011)
  • Conference scholarship of the Federal Ministry of Education and Research (2010)
  • Workshop scholarship by the European Commission Marie Curie Program (2009)
  • Scholarship of the German Academic Exchange Service (DAAD) for integrated studies abroad at the University of Wisconsin, Madison, USA (2006-2007)

Reviewer for 

  • Automatica
  • Bioinformatics
  • BioSystems
  • Biotechnology Progress
  • BMC Systems Biology
  • Current Opinions in Biotechnology
  • Conference of Decision and Control
  • EURASIP Journal on Bioinformatics and Systems Biology
  • IET Systems Biology
  • IEEE Conference on Decision and Control
  • IFAC Conference on Foundations of Systems Biology in Engineering
  • Journal of Chemical Physics
  • Journal of Mathematical Biology
  • Journal of Mathematical Modeling of Natural Phenomena
  • Journal of Theoretical Biology
  • Nature Communications
  • npj Systems Biology and Applications
  • PLoS Computational Biology
  • PLoS ONE


As the development of high-quality, easy-to-use software is essential to computational sciences forward, I contributed to the development of several toolboxes, i.e.

  • AMICI: An Advanced Matlab Interface for CVODES and IDAS for the numerical simulation and sensitivity analysis of high-dimensional ODE models with events.
  • Data2Dynamics: A collection of numerical methods for quantitative dynamic modeling and a comprehensive model and data description language.
  • ShAPE-DALSP: A toolbox for the simulation and parameter estimation of division-, age- and label-structured population models.
  • MEMO: A toolbox for multi-experiment mixture modeling of single-cell snapshot data.
  • iVUN:  A visual analytics system supporting uncertainty-aware analysis of static and dynamic attributes of biochemical reaction networks modeled by ordinary differential equations.
  • BioSDP: A toolbox for the analysis of uncertain biochemical networks via semidefinite programming.