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]
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).
[2]
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).
[3]
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).
[4]
H. Wickham. Tidy data. Journal of statistical software 59, 1–23 (2014).
[5]
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).
[6]
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).
[7]
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).
[8]
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).
[9]
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).
[10]
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).
[11]
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).
[12]
M. Vihola. Ergonomic and reliable Bayesian inference with adaptive Markov chain Monte Carlo. Wiley statsRef: statistics reference online, 1–12 (2014).
[13]
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).
[14]
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).
[15]
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).
[16]
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).
[17]
M. Blondel and V. Roulet. The elements of differentiable programming, arXiv preprint arXiv:2403.14606 (2024).
[18]
J. Revels, M. Lubin and T. Papamarkou. Forward-mode automatic differentiation in Julia, arXiv preprint arXiv:1607.07892 (2016).
[19]
R. Mester, A. Landeros, C. Rackauckas and K. Lange. Differential methods for assessing sensitivity in biological models. PLoS computational biology 18, e1009598 (2022).
[20]
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).
[21]
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.
[22]
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).
[23]
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).
[24]
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).
[25]
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).