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. doi:10.1038/s41598-022-26483-6.

  2. Joy Armistead, Sebastian Höpfl, Pierre Goldhausen, Andrea Müller-Hartmann, Evelin Fahle, Julia Hatzold, Rainer Franzen, Susanne Brodesser, Nicole E. Radde, and Matthias Hammerschmidt. A sphingolipid rheostat controls apoptosis versus apical cell extrusion as alternative tumour-suppressive mechanisms. Cell Death & Disease, October 2024. doi:10.1038/s41419-024-07134-2.

  3. Jonas Arruda, Niels Bracher, Ullrich Köthe, Jan Hasenauer, and Stefan T. Radev. Diffusion models in simulation-based inference: a tutorial review. 2025. URL: https://arxiv.org/abs/2512.20685, arXiv:2512.20685.

  4. Jonas Arruda, Yannik Schälte, Clemens Peiter, Olga Teplytska, Ulrich Jaehde, and Jan Hasenauer. An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation. bioRxiv, 2023. arXiv:https://www.biorxiv.org/content/early/2023/08/23/2023.08.22.554273.full.pdf, doi:10.1101/2023.08.22.554273.

  5. Aaron Callenbach, Domagoj Dorešić, Robert Düster, Vanessa Nakonecnij, Erika Dudkin, Matthias Geyer, and Jan Hasenauer. Quantitative modeling of P-TEFb mediated CTD phosphorylation identifies local cooperativity. bioRxiv, 2025. URL: https://www.biorxiv.org/content/early/2025/12/02/2025.11.29.690978, arXiv:https://www.biorxiv.org/content/early/2025/12/02/2025.11.29.690978.full.pdf, doi:10.64898/2025.11.29.690978.

  6. 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. arXiv:https://www.medrxiv.org/content/early/2021/10/01/2021.10.01.21263052.full.pdf, doi:10.1101/2021.10.01.21263052.

  7. Domagoj Dorešić, Stephan Grein, and Jan Hasenauer. Efficient parameter estimation for ode models of cellular processes using semi-quantitative data. bioRxiv, 2024. arXiv:https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371.full.pdf, doi:10.1101/2024.01.26.577371.

  8. 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. doi:10.1145/3468264.3468615.

  9. Ariane Ernst, Anastasia Bankowski, Meida Jusyte, Toluwani Okunola, Tino Petrov, Alexander M. Walter, and Stefanie Winkelmann. Parameter optimization for a neurotransmission recovery model. Bulletin of Mathematical Biology, July 2025. doi:10.1007/s11538-025-01486-2.

  10. Carles Falcó, Daniel J Cohen, José A Carrillo, and Ruth E Baker. Quantifying tissue growth, shape and collision via continuum models and bayesian inference. Journal of the Royal Society Interface, 20(1):1–12, 07 2023. doi:10.1098/rsif.2023.0184.

  11. Carles Falcó, Daniel J. Cohen, José A. Carrillo, and Ruth E. Baker. Quantifying cell cycle regulation by tissue crowding. Biophysical Journal, May 2024. doi:10.1016/j.bpj.2024.05.003.

  12. Sophie Fischer-Holzhausen and Susanna Röblitz. A workflow for incorporating cross-sectional data into the calibration of dynamic models. bioRxiv, 2023. arXiv:https://www.biorxiv.org/content/early/2023/01/19/2023.01.17.523407.full.pdf, doi:10.1101/2023.01.17.523407.

  13. 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. arXiv:https://www.biorxiv.org/content/early/2022/02/18/2022.02.17.480899.full.pdf, doi:10.1101/2022.02.17.480899.

  14. 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. doi:10.1371/journal.pcbi.1010322.

  15. 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. doi:10.1016/j.cels.2020.10.002.

  16. Sebastian Höpfl, Mohamed Albadry, Uta Dahmen, Karl-Heinz Herrmann, Eva Marie Kindler, Matthias König, Jürgen Rainer Reichenbach, Hans-Michael Tautenhahn, Weiwei Wei, Wan-Ting Zhao, and Nicole Erika Radde. Bayesian modelling of time series data (BayModTS) - a FAIR workflow to process sparse and highly variable data. Bioinformatics, pages btae312, 05 2024. arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae312/57572667/btae312.pdf, doi:10.1093/bioinformatics/btae312.

  17. Sebastian Höpfl, Merih Özverin, Helena Nowack, Raluca Tamas, Andrew G. Clark, Nicole Radde, and Monilola A. Olayioye. Integrated mathematical and experimental modeling uncovers enhanced emt plasticity upon loss of the dlc1 tumor suppressor. PLOS Computational Biology, 21(5):1–20, 05 2025. URL: https://doi.org/10.1371/journal.pcbi.1013076, doi:10.1371/journal.pcbi.1013076.

  18. Clayton Jackson, Matthieu Chardon, Y. Curtis Wang, Johann Rudi, Matthew Tresch, Charles J. Heckman, and Roger D. Quinn. Multimodal parameter inference for a canonical motor microcircuit controlling rat hindlimb motion. In Fabian Meder, Alexander Hunt, Laura Margheri, Anna Mura, and Barbara Mazzolai, editors, Biomimetic and Biohybrid Systems, 38–51. Cham, 2023. Springer Nature Switzerland. doi:10.1007/978-3-031-39504-8_3.

  19. Paul Jonas Jost, Daniel Weindl, Klaus Wunderling, Christoph Thiele, and Jan Hasenauer. Pseudo-time trajectory of single-cell lipidomics: suggestion for experimental setup and computational analysis. bioRxiv, 2025. URL: https://www.biorxiv.org/content/early/2025/04/17/2025.04.11.648323, arXiv:https://www.biorxiv.org/content/early/2025/04/17/2025.04.11.648323.full.pdf, doi:10.1101/2025.04.11.648323.

  20. Anna E Kiss, Anuroop V Venkatasubramani, Dilan Pathirana, Silke Krause, Aline Campos Sparr, Jan Hasenauer, Axel Imhof, Marisa Müller, and Peter B Becker. Processivity and specificity of histone acetylation by the male-specific lethal complex. Nucleic Acids Research, pages gkae123, 02 2024. arXiv:https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkae123/56756494/gkae123.pdf, doi:10.1093/nar/gkae123.

  21. Polina Lakrisenko, Jörg Isensee, Tim Hucho, Daniel Weindl, and Jan Hasenauer. A mechanistic model of protein kinase A dynamics under pro- and anti-nociceptive inputs. bioRxiv, 2026. URL: https://www.biorxiv.org/content/early/2026/02/14/2026.02.12.705506, arXiv:https://www.biorxiv.org/content/early/2026/02/14/2026.02.12.705506.full.pdf, doi:10.64898/2026.02.12.705506.

  22. Polina Lakrisenko, Dilan Pathirana, Daniel Weindl, and Jan Hasenauer. Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states. PLOS ONE, 19(10):1–19, 10 2024. doi:10.1371/journal.pone.0312148.

  23. 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. doi:10.1371/journal.pcbi.1010783.

  24. Simon Merkt, Solomon Ali, Esayas Kebede Gudina, Wondimagegn Adissu, Addisu Gize, Maximilian Muenchhoff, Alexander Graf, Stefan Krebs, Kira Elsbernd, Rebecca Kisch, Sisay Sirgu Betizazu, Bereket Fantahun, Delayehu Bekele, Raquel Rubio-Acero, Mulatu Gashaw, Eyob Girma, Daniel Yilma, Ahmed Zeynudin, Ivana Paunovic, Michael Hoelscher, Helmut Blum, Jan Hasenauer, Arne Kroidl, and Andreas Wieser. Long-term monitoring of SARS-CoV-2 seroprevalence and variants in Ethiopia provides prediction for immunity and cross-immunity. Nature Communications, April 2024. doi:10.1038/s41467-024-47556-2.

  25. 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. doi:10.1016/j.ymben.2022.11.003.

  26. Ben Noordijk, Marcel Reinders, Aalt D.J. van Dijk, and Dick de Ridder. BADDADAN: mechanistic modelling of time series gene module expression. bioRxiv, 2025. URL: https://www.biorxiv.org/content/early/2025/02/23/2025.02.18.638670, arXiv:https://www.biorxiv.org/content/early/2025/02/23/2025.02.18.638670.full.pdf, doi:10.1101/2025.02.18.638670.

  27. Dilan Pathirana, Frank T. Bergmann, Domagoj Doresic, Polina Lakrisenko, Sebastian Persson, Niklas Neubrand, Jens Timmer, Clemens Kreutz, Harald Binder, Marija Cvijovic, Daniel Weindl, and Jan Hasenauer. PEtab Select: specification standard and supporting software for automated model selection. bioRxiv, 2025. URL: https://www.biorxiv.org/content/early/2025/05/15/2025.05.12.653312, arXiv:https://www.biorxiv.org/content/early/2025/05/15/2025.05.12.653312.full.pdf, doi:10.1101/2025.05.12.653312.

  28. Sebastian Persson, Fabian Fröhlich, Stephan Grein, Torkel Loman, Damiano Ognissanti, Viktor Hasselgren, Jan Hasenauer, and Marija Cvijovic. PEtab.jl: advancing the efficiency and utility of dynamic modelling. Bioinformatics, 41(9):btaf497, 09 2025. arXiv:https://academic.oup.com/bioinformatics/article-pdf/41/9/btaf497/64232071/btaf497.pdf, doi:10.1093/bioinformatics/btaf497.

  29. Maren Philipps, Antonia Körner, Jakob Vanhoefer, Dilan Pathirana, and Jan Hasenauer. Non-negative universal differential equations with applications in systems biology. IFAC-PapersOnLine, 58(23):25–30, 2024. doi:10.1016/j.ifacol.2024.10.005.

  30. Maren Philipps, Nina Schmid, and Jan Hasenauer. Current state and open problems in universal differential equations for systems biology. npj Systems Biology and Applications, August 2025. doi:10.1038/s41540-025-00550-w.

  31. Leonard Schmiester, Fara Brasó-Maristany, Blanca González-Farré, Tomás Pascual, Joaquín Gavilá, Xavier Tekpli, Jürgen Geisler, Vessela N. Kristensen, Arnoldo Frigessi, Aleix Prat, and Alvaro Köhn-Luque. Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition. Clinical Cancer Research, pages OF1–OF9, 07 2024. arXiv:https://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-24-0244/3478451/ccr-24-0244.pdf, doi:10.1158/1078-0432.CCR-24-0244.

  32. 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, Alejandro F. Timmer, Jens 2and 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. doi:10.1371/journal.pcbi.1008646.

  33. 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. doi:10.1007/s00285-020-01522-w.

  34. 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. arXiv:https://academic.oup.com/bioinformatics/article-pdf/37/23/4493/41641709/btab512.pdf, doi:10.1093/bioinformatics/btab512.

  35. Matthew Speers, Jonathan Angus Tawn, and Philip Jonathan. Sequential design for the efficient estimation of offshore structure failure probability. 2025. URL: https://arxiv.org/abs/2509.18319, arXiv:2509.18319.

  36. Jakob Vanhoefer, Antonia Körner, Domagoj Doresic, Jan Hasenauer, and Dilan Pathirana. Scalable branch-and-bound model selection with non-monotonic criteria including aic, bic and mallows's $\mathit C_p$. 2025. URL: https://arxiv.org/abs/2512.12221, arXiv:2512.12221.

  37. Gonzalo Vivares, Jan Dijkstra, and Andre Bannink. Modeling diurnal rumen metabolism dynamics in dairy cattle: an update to a mechanistic model representing eating behavior, rumen content, rumination, and acid-base balance. Journal of Dairy Science, 108(7):6934–6957, Jul 2025. doi:10.3168/jds.2024-26121.

  38. Vincent Wieland and Jan Hasenauer. A stochastic modelling framework for cancer patient trajectories: combining tumour growth, metastasis, and survival. Journal of Mathematical Biology, May 2025. doi:10.1007/s00285-025-02229-6.