Carolin Loos
PhD candidate

Phone: +49 89 3187-3272
Building/Room: 58a / 001


Short CV

I studied Mathematics at the University of Ulm, the Boğaziçi University in Istanbul and the Technische Universität München. After obtaining my M.Sc. in 2015, I started as a PhD student at the Institute of Computational Biology.

Current research

  • Robust and efficient parameter estimation for ordinary differential equation (ODE) models
  • Mechanistic modeling of single-cell populations to unravel sources of heterogeneity
  • Applications in, e.g., pain sensitization, histone methylation and viral infection


  1. Fröhlich, F., Loos, C., & Hasenauer, J. (2019). Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes. in Gene Regulatory Networks (pp. 385-422). Humana Press, New York, NY.
  2. Hass, H.*, Loos, C.*, Raimundez-Alvarez, E., Timmer, J., Hasenauer, J., & Kreutz, C. (2019). Benchmark Problems for Dynamic Modeling of Intracellular Processes. Bioinformatics, btz020 (*equal contribution)
  3. Sinzger, M., Vanhoefer, J., Loos, C., & Hasenauer, J. (2018). Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible. bioRxiv, 409946. accepted for publication in Scientific Reports.
  4. Loos, C.*, Moeller, K.*, Fröhlich, F., Hucho, T., & Hasenauer, J. (2018). A hierarchical, data-driven approach for modeling single-cell populations predicts latent causes of cell-to-cell variability. Cell Systems, 6(5), 593-603.
  5. Loos, C.*, Krause, S.*, & Hasenauer, J. (2018). Hierarchical optimization for the efficient parametrization of ODE models. Bioinformatics, 34(24), 4266-4273.
  6. Stapor, P., Weindl, D., Ballnus, B, Hug, S., Loos, C., Fiedler, A., Krause, S., Hroß, S., Fröhlich, F., & Hasenauer, J. (2017) PESTO: Parameter Estimation TOolbox. Bioinformatics, 34(4), 705-707.
  7. Maier, C., Loos, C., Hasenauer, J. (2017). Robust parameter estimation for dynamical systems from outlier-corrupted data. Bioinformatics, 33(5), 718-725.
  8. Loos, C., Fiedler, A., & Hasenauer, J. (2016). Parameter estimation for reaction rate equation constrained mixture models. Lecture Notes Comp. Sci. 9859, 186-200.
  9. Loos, C. (2016). Analysis of single-cell data: ODE constrained mixture modeling and approximate Bayesian computation. BestMasters, Springer Spektrum.
  10. Loos, C., Marr, C., Theis, F. J., & Hasenauer, J. (2015). Approximate Bayesian computation for stochastic single-cell time-lapse data using multivariate test statistics. Lecture Notes Comp. Sci. 9308, 52-63.