Herr Dipl.-Biol. (t.o.)

Karsten Kuritz

Wissenschaftlicher Mitarbeiter
Institut für Systemtheorie und Regelungstechnik


+49 711 685-67757
+49 711 685-67735

Pfaffenwaldring 9
70569 Stuttgart
Raum: 3.244


Nach Vereinbarung


 Autonomous Systems, Ensemble Control, Systems Biology

In particular: Cell decision processes in heterogeneous cell populations

  1. 2019

    1. Kuritz, Karsten, Stöhr, D., Maichl, D., Pollak, N., Rehm, M., & Allgöwer, F. (2019). Reconstructing temporal and spatial dynamics in single-cell experiments. BioRxiv. https://doi.org/10.1101/697151
    2. Kuritz, K., Zeng, S., & Allgöwer, F. (2019). Ensemble Controllability of Cellular Oscillators. IEEE Control Systems Letters, 3(2), 296–301. https://doi.org/10.1109/LCSYS.2018.2870967
  2. 2018

    1. Kuritz, Karsten, Halter, W., & Allgöwer, F. (2018). Passivity-based ensemble control for cell cycle synchronization. In R. Tempo, S. Yurkovich, & P. Misra (Eds.), Emerg. Appl. Control Syst. Theory (1st ed.). Springer International Publishing. http://www.springer.com/de/book/9783319670676
    2. Kuritz, K., Imig, D., Dyck, M., & Allgöwer, F. (2018). Ensemble control for cell cycle synchronization of heterogeneous cell populations. IFAC-PapersOnLine, 51(19), 44–47. https://doi.org/10.1016/j.ifacol.2018.09.034
  3. 2017

    1. Kuritz, K., Stöhr, D., Pollak, N., & Allgöwer, F. (2017). On the relationship between cell cycle analysis with ergodic principles        and age-structured cell population models. J. Theor. Biol., 414, 91–102. https://doi.org/10.1016/j.jtbi.2016.11.024
    2. Thomaseth, C., Kuritz, K., Allgöwer, F., & Radde, N. (2017). The circuit-breaking algorithm for monotone systems. Math. Biosci., 284, 80--91. https://doi.org/10.1016/j.mbs.2016.09.002
since 11/2012 Research assistant at the Institute for Systems Theory and Automatic Control
08/2012 Diploma in Technical Biology
2006-2012 Studies in Technical Biology at the Universität Stuttgart, Germany
2003 Abitur at Parler-Gymnasium in Schwäbisch Gmünd, Germany
  • Estimating pseudotime orthogonal signalling strength from mutual information, Bachelor Thesis
  • Implementation of a Controller for Cell Cycle Synchronization, Bachelor Thesis
  • Dynamics of extrinsically induced apoptosis during cell cycle progression, Bachelor Thesis
  • Connecting single cell methods to cell cycle models, Master Thesis
  • The role of phosphatases for Heregulin induced stimulation of AKT – a modeling study, Bachelor Thesis
  • Cell age- and tumour size-structured population model for cancer spheroids, Bachelor Thesis
  • Modelling the Autophagy Network, Bachelor Thesis
  • Determining the cell cyle-dependent levels of cleaved Caspase-8/-3 in Db-scTRAIL stimulated NCI-H460 cells using snapshot data of flow cytometry measurements, Diploma Thesis
  • Analysis of the Musculoskeletal System with switching Dynamics, Diploma Thesis

Autonomous E-Scooters

E-Scooter sharing systems are widely used for convenient short distance travels. These sharing systems however face several problems, like bad spatial coverage, need for juicers (people charging the scooters) and high maintenance costs. By developing scooters with autonomous driving and navigational capabilities we aim to overcome these challenges.


Analysis and Control of heterogenous cell populations

Multiple aberrations in various cellular signaling pathways accumulate in the development of cancer. Many dysregulations occur in cellular pathways, that are activated in response to stress related stimuli such as DNA damage, nutrient starvation or death receptor ligands. Activity in these cellular signaling pathways causes cell fate decisions towards cell cycle arrest, senescence, autophagy or apoptosis. The signaling pathways furthermore affect each other and this interaction represents cell intrinsic control mechanisms.Together with our collaborators  we aim at a deeper understanding of these cellular control mechanisms.
We follow this aim by an integrative approach, where we combine methods from data analysis, biological system modeling and system theory.
The focus of our data analysis, in particular single cell and population data from fluorescence microscopy or FACS experiments, is to identify subpopulations (e.g. drug sensitive or resistant) within the biological sample which behave differently in response to some treatment (e.g. drug sensitive or resistant). The data from different subpopulations is then analyzed with the help of mathematical models that describe the process. The model classes that we use for this purpose ranges from classical ordinary differential equation (ODE) to partial differential equations (PDE) and stochastic differential equations (SDE) models.
The comparison of the model parameters, structure and predictions, that we get from the various subpopulations, allows us to identify crucial processes that cause the emergence of subpopulations. The knowledge that we ultimately obtain from this work flow helps to identify new drug targets, and to optimize treatment strategies.



  • Practical Course: Biological Systems (Wintersemester 17/18: Dozent)
  • Introduction to Adaptive Control (Wintersemester 17/18: Vorlesungsassistent)
  • Systems Theory in Systems Biology (Sommersemester 17: Dozent)
  • Nonlinear Dynamics (Sommersemester 15: Vorlesungsassistent)
  • Statistical Learning Methods and Stochastic Control (Sommersemester 14, 15: Vorlesungsassistent)
  • Systemdynamische Grundlagen der Regelungstechnik (Sommersemester 13, 14: Vorlesungsassistent)
  • Matlab Einführungskurs (Wintersemester 13/14, 14/15, 15/16, 16/17, 17/18: Organizer)
  • Praktikum: Grüne Systembiologie (Sommersemester 13: Dozent)
  • Grundlagen der Systembiologie (Wintersemester 12/13: Dozent)
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