PEtab.jl

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References ​

Throughout the documentation, references related to parameter estimation for ODE models can be found. This page contains a complete list of all references mentioned in the documentation.

  1. S. Persson, F. Fröhlich, S. Grein, T. Loman, D. Ognissanti, V. Hasselgren, J. Hasenauer and M. Cvijovic. PEtab. jl: advancing the efficiency and utility of dynamic modelling. Bioinformatics 41, btaf497 (2025).

  2. F. Fröhlich and P. K. Sorger. Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models. PLoS computational biology 18, e1010322 (2022).

  3. A. Raue, M. Schilling, J. Bachmann, A. Matteson, M. Schelke, D. Kaschek, S. Hug, C. Kreutz, B. D. Harms, F. J. Theis and others. Lessons learned from quantitative dynamical modeling in systems biology. PloS one 8, e74335 (2013).

  4. H. Hass, C. Loos, E. Raimundez-Alvarez, J. Timmer, J. Hasenauer and C. Kreutz. Benchmark problems for dynamic modeling of intracellular processes. Bioinformatics 35, 3073–3082 (2019).

  5. H. Wickham. Tidy data. Journal of statistical software 59, 1–23 (2014).

  6. A. F. Villaverde, F. Fröhlich, D. Weindl, J. Hasenauer and J. R. Banga. Benchmarking optimization methods for parameter estimation in large kinetic models. Bioinformatics 35, 830–838 (2019).

  7. F. Fröhlich, F. J. Theis, J. O. Rädler and J. Hasenauer. Parameter estimation for dynamical systems with discrete events and logical operations. Bioinformatics 33, 1049–1056 (2017).

  8. L. Schmiester, Y. Schälte, F. T. Bergmann, T. Camba, E. Dudkin, J. Egert, F. Fröhlich, L. Fuhrmann, A. L. Hauber, S. Kemmer and others. PEtab—Interoperable specification of parameter estimation problems in systems biology. PLoS computational biology 17, e1008646 (2021).

  9. P. J. Jost, F. T. Bergmann, D. Weindl and J. Hasenauer. PEtab-GUI: A graphical user interface to create, edit and inspect PEtab parameter estimation problems, arXiv preprint arXiv:2511.11515 (2025).

  10. M. E. Boehm, L. Adlung, M. Schilling, S. Roth, U. Klingmüller and W. D. Lehmann. Identification of isoform-specific dynamics in phosphorylation-dependent STAT5 dimerization by quantitative mass spectrometry and mathematical modeling. Journal of proteome research 13, 5685–5694 (2014).

  11. D. Pathirana, F. T. Bergmann, D. Doresic, P. Lakrisenko, S. Persson, N. Neubrand, J. Timmer, C. Kreutz, H. Binder, M. Cvijovic and others. PEtab Select: specification standard and supporting software for automated model selection, bioRxiv, 2025–05 (2025).

  12. A. F. Villaverde, D. Pathirana, F. Fröhlich, J. Hasenauer and J. R. Banga. A protocol for dynamic model calibration. Briefings in bioinformatics 23, bbab387 (2022).

  13. A. Wächter and L. T. Biegler. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical programming 106, 25–57 (2006).

  14. M. Gabel, T. Hohl, A. Imle, O. T. Fackler and F. Graw. FAMoS: A Flexible and dynamic Algorithm for Model Selection to analyse complex systems dynamics. PLOS Computational Biology 15, e1007230 (2019).

  15. M. Vihola. Ergonomic and reliable Bayesian inference with adaptive Markov chain Monte Carlo. Wiley statsRef: statistics reference online, 1–12 (2014).

  16. M. D. Hoffman, A. Gelman and others. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15, 1593–1623 (2014).

  17. B. Carpenter, A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. A. Brubaker, J. Guo, P. Li and A. Riddell. Stan: A probabilistic programming language. Journal of statistical software 76 (2017).

  18. A. Gelman, A. Vehtari, D. Simpson, C. C. Margossian, B. Carpenter, Y. Yao, L. Kennedy, J. Gabry, P.-C. Bürkner and M. Modrák. Bayesian workflow, arXiv preprint arXiv:2011.01808 (2020).

  19. P. Städter, Y. Schälte, L. Schmiester, J. Hasenauer and P. L. Stapor. Benchmarking of numerical integration methods for ODE models of biological systems. Scientific reports 11, 2696 (2021).

  20. F. Sapienza, J. Bolibar, F. Schäfer, B. Groenke, A. Pal, V. Boussange, P. Heimbach, G. Hooker, F. Pérez, P.-O. Persson and others. Differentiable Programming for Differential Equations: A Review, arXiv preprint arXiv:2406.09699 (2024).

  21. M. Blondel and V. Roulet. The elements of differentiable programming, arXiv preprint arXiv:2403.14606 (2024).

  22. J. Revels, M. Lubin and T. Papamarkou. Forward-mode automatic differentiation in Julia, arXiv preprint arXiv:1607.07892 (2016).

  23. R. Mester, A. Landeros, C. Rackauckas and K. Lange. Differential methods for assessing sensitivity in biological models. PLoS computational biology 18, e1009598 (2022).

  24. F. Fröhlich, B. Kaltenbacher, F. J. Theis and J. Hasenauer. Scalable parameter estimation for genome-scale biochemical reaction networks. PLoS computational biology 13, e1005331 (2017).

  25. Y. Ma, V. Dixit, M. J. Innes, X. Guo and C. Rackauckas. A comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions. In: 2021 IEEE High Performance Extreme Computing Conference (HPEC) (IEEE, 2021); pp. 1–9.

  26. A. Raue, B. Steiert, M. Schelker, C. Kreutz, T. Maiwald, H. Hass, J. Vanlier, C. Tönsing, L. Adlung, R. Engesser and others. Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems. Bioinformatics 31, 3558–3560 (2015).

  27. R. Beer, K. Herbst, N. Ignatiadis, I. Kats, L. Adlung, H. Meyer, D. Niopek, T. Christiansen, F. Georgi, N. Kurzawa and others. Creating functional engineered variants of the single-module non-ribosomal peptide synthetase IndC by T domain exchange. Molecular BioSystems 10, 1709–1718 (2014).

  28. J. Bachmann, A. Raue, M. Schilling, M. E. Böhm, C. Kreutz, D. Kaschek, H. Busch, N. Gretz, W. D. Lehmann, J. Timmer and others. Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range. Molecular systems biology 7, 516 (2011).