Publications using pypesto

pypesto was used in the following publications:

  1. Mohamed Albadry, Sebastian Höpfl, Nadia Ehteshamzad, Matthias König, Michael Böttcher, Jasna Neumann, Amelie Lupp, Olaf Dirsch, Nicole Radde, Bruno Christ, Madlen Christ, Lars Ole Schwen, Hendrik Laue, Robert Klopfleisch, and Uta Dahmen. Periportal steatosis in mice affects distinct parameters of pericentral drug metabolism. Scientific Reports, 12(1):21825, 2022. URL:, doi:10.1038/s41598-022-26483-6.

  2. Lorenzo Contento, Noemi Castelletti, Elba Raimúndez, Ronan Le Gleut, Yannik Schälte, Paul Stapor, Ludwig Christian Hinske, Michael Hölscher, Andreas Wieser, Katja Radon, Christiane Fuchs, and Jan Hasenauer. Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates. medRxiv, 2021. URL:, arXiv:, doi:10.1101/2021.10.01.21263052.

  3. Saikat Dutta, August Shi, and Sasa Misailovic. Flex: fixing flaky tests in machine learning projects by updating assertion bounds. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2021, 603–614. New York, NY, USA, 2021. Association for Computing Machinery. URL:, doi:10.1145/3468264.3468615.

  4. Sophie Fischer-Holzhausen and Susanna Röblitz. A workflow for incorporating cross-sectional data into the calibration of dynamic models. bioRxiv, 2023. URL:, arXiv:, doi:10.1101/2023.01.17.523407.

  5. Fabian Fröhlich, Luca Gerosa, Jeremy Muhlich, and Peter K. Sorger. Mechanistic model of mapk signaling reveals how allostery and rewiring contribute to drug resistance. bioRxiv, 2022. URL:, arXiv:, doi:10.1101/2022.02.17.480899.

  6. Fabian Fröhlich and Peter K. Sorger. Fides: reliable trust-region optimization for parameter estimation of ordinary differential equation models. PLOS Computational Biology, 18(7):1–28, 07 2022. URL:, doi:10.1371/journal.pcbi.1010322.

  7. Luca Gerosa, Christopher Chidley, Fabian Fröhlich, Gabriela Sanchez, Sang Kyun Lim, Jeremy Muhlich, Jia-Yun Chen, Sreeram Vallabhaneni, Gregory J. Baker, Denis Schapiro, Mariya I. Atanasova, Lily A. Chylek, Tujin Shi, Lian Yi, Carrie D. Nicora, Allison Claas, Thomas S. C. Ng, Rainer H. Kohler, Douglas A. Lauffenburger, Ralph Weissleder, Miles A. Miller, Wei-Jun Qian, H. Steven Wiley, and Peter K. Sorger. Receptor-driven erk pulses reconfigure mapk signaling and enable persistence of drug-adapted braf-mutant melanoma cells. Cell Systems, 11(5):478–494.e9, November 2020. URL:, doi:10.1016/j.cels.2020.10.002.

  8. Polina Lakrisenko, Paul Stapor, Stephan Grein, Łukasz Paszkowski, Dilan Pathirana, Fabian Fröhlich, Glenn Terje Lines, Daniel Weindl, and Jan Hasenauer. Efficient computation of adjoint sensitivities at steady-state in ode models of biochemical reaction networks. PLOS Computational Biology, 19(1):1–19, 01 2023. URL:, doi:10.1371/journal.pcbi.1010783.

  9. Shekhar Mishra, Ziyu Wang, Michael J. Volk, and Huimin Zhao. Design and application of a kinetic model of lipid metabolism in saccharomyces cerevisiae. Metabolic Engineering, 75:12–18, 2023. URL:, doi:

  10. Leonard Schmiester, Yannik Schälte, Frank T. Bergmann, Tacio Camba, Erika Dudkin, Janine Egert, Fabian Fröhlich, Lara Fuhrmann, Adrian L. Hauber, Svenja Kemmer, Polina Lakrisenko, Carolin Loos, Simon Merkt, Wolfgang Müller, Dilan Pathirana, Elba Raimúndez, Lukas Refisch, Marcus Rosenblatt, Paul L. Stapor, Philipp Städter, Dantong Wang, Franz-Georg Wieland, Julio R. Banga, Jens Timmer, Alejandro F. Villaverde, Sven Sahle, Clemens Kreutz, Jan Hasenauer, and Daniel Weindl. Petab—interoperable specification of parameter estimation problems in systems biology. PLOS Computational Biology, 17(1):1–10, 01 2021. URL:, doi:10.1371/journal.pcbi.1008646.

  11. Leonard Schmiester, Daniel Weindl, and Jan Hasenauer. Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach. Journal of Mathematical Biology, 81(2):603–623, 2020. URL:, doi:10.1007/s00285-020-01522-w.

  12. Leonard Schmiester, Daniel Weindl, and Jan Hasenauer. Efficient gradient-based parameter estimation for dynamic models using qualitative data. Bioinformatics, 37(23):4493–4500, 07 2021. URL:, arXiv:, doi:10.1093/bioinformatics/btab512.