List of publications using AMICI. Total number is 51.

If you applied AMICI in your work and your publication is missing, please let us know via a new Github issue.


Raimúndez, Elba, Erika Dudkin, Jakob Vanhoefer, Emad Alamoudi, Simon Merkt, Lara Fuhrmann, Fan Bai, and Jan Hasenauer. 2021. “COVID-19 Outbreak in Wuhan Demonstrates the Limitations of Publicly Available Case Numbers for Epidemiological Modeling.” Epidemics 34: 100439.

Schmiester, Leonard, Daniel Weindl, and Jan Hasenauer. 2021. “Efficient Gradient-Based Parameter Estimation for Dynamic Models Using Qualitative Data.” bioRxiv.

Städter, Philipp, Yannik Schälte, Leonard Schmiester, Jan Hasenauer, and Paul L. Stapor. 2021. “Benchmarking of Numerical Integration Methods for Ode Models of Biological Systems.” Scientific Reports 11 (1): 2696.


Alabert, Constance, Carolin Loos, Moritz Voelker-Albert, Simona Graziano, Ignasi Forné, Nazaret Reveron-Gomez, Lea Schuh, et al. 2020. “Domain Model Explains Propagation Dynamics and Stability of Histone H3k27 and H3k36 Methylation Landscapes.” Cell Reports 30 (4): 1223–1234.e8.

Erdem, Cemal, Ethan M. Bensman, Arnab Mutsuddy, Michael M. Saint-Antoine, Mehdi Bouhaddou, Robert C. Blake, Will Dodd, et al. 2020. “A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models.” bioRxiv.

Gerosa, Luca, Christopher Chidley, Fabian Fröhlich, Gabriela Sanchez, Sang Kyun Lim, Jeremy Muhlich, Jia-Yun Chen, et al. 2020. “Receptor-Driven Erk Pulses Reconfigure Mapk Signaling and Enable Persistence of Drug-Adapted Braf-Mutant Melanoma Cells.” Cell Systems.

Kuritz, Karsten, Alain R Bonny, João Pedro Fonseca, and Frank Allgöwer. 2020. “PDE-Constrained Optimization for Estimating Population Dynamics over Cell Cycle from Static Single Cell Measurements.” bioRxiv.

Schälte, Yannik, and Jan Hasenauer. 2020. “Efficient Exact Inference for Dynamical Systems with Noisy Measurements Using Sequential Approximate Bayesian Computation.” bioRxiv.

Schmiester, Leonard, Daniel Weindl, and Jan Hasenauer. 2020. “Parameterization of Mechanistic Models from Qualitative Data Using an Efficient Optimal Scaling Approach.” Journal of Mathematical Biology, July.

Schmucker, Robin, Gabriele Farina, James Faeder, Fabian Fröhlich, Ali Sinan Saglam, and Tuomas Sandholm. 2020. “Combination Treatment Optimization Using a Pan-Cancer Pathway Model.” bioRxiv.

Schuh, Lea, Carolin Loos, Daniil Pokrovsky, Axel Imhof, Ralph Rupp, and Carsten Marr. 2020. “Computational Modeling Reveals Cell-Cycle Dependent Kinetics of H4k20 Methylation States During Xenopus Embryogenesis.” bioRxiv.

Sten, Sebastian. 2020. “Mathematical Modeling of Neurovascular Coupling.” Linköping University Medical Dissertations. PhD thesis, Linköping UniversityLinköping UniversityLinköping University, Division of Diagnostics; Specialist Medicine, Faculty of Medicine; Health Sciences, Center for Medical Image Science; Visualization (CMIV); Linköping University, Division of Diagnostics; Specialist Medicine.

Sten, Sebastian, Fredrik Elinder, Gunnar Cedersund, and Maria Engström. 2020. “A Quantitative Analysis of Cell-Specific Contributions and the Role of Anesthetics to the Neurovascular Coupling.” NeuroImage 215: 116827.

Tsipa, Argyro, Jake Alan Pitt, Julio R. Banga, and Athanasios Mantalaris. 2020. “A Dual-Parameter Identification Approach for Data-Based Predictive Modeling of Hybrid Gene Regulatory Network-Growth Kinetics in Pseudomonas Putida Mt-2.” Bioprocess and Biosystems Engineering 43 (9): 1671–88.


Adlung, Lorenz, Paul Stapor, Christian Tönsing, Leonard Schmiester, Luisa E. Schwarzmüller, Dantong Wang, Jens Timmer, Ursula Klingmüller, Jan Hasenauer, and Marcel Schilling. 2019. “Cell-to-Cell Variability in Jak2/Stat5 Pathway Components and Cytoplasmic Volumes Define Survival Threshold in Erythroid Progenitor Cells.” bioRxiv.

Dharmarajan, Lekshmi, Hans-Michael Kaltenbach, Fabian Rudolf, and Joerg Stelling. 2019. “A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics.” Cell Systems 8 (1): 15–26.e11.

Fischer, David S., Anna K. Fiedler, Eric Kernfeld, Ryan M. J. Genga, Aimée Bastidas-Ponce, Mostafa Bakhti, Heiko Lickert, Jan Hasenauer, Rene Maehr, and Fabian J. Theis. 2019. “Inferring Population Dynamics from Single-Cell Rna-Sequencing Time Series Data.” Nature Biotechnology 37: 461–68.

Gregg, Robert W, Saumendra N Sarkar, and Jason E Shoemaker. 2019. “Mathematical Modeling of the cGAS Pathway Reveals Robustness of Dna Sensing to Trex1 Feedback.” Journal of Theoretical Biology 462 (February): 148–57.

Kapfer, Eva-Maria, Paul Stapor, and Jan Hasenauer. 2019. “Challenges in the Calibration of Large-Scale Ordinary Differential Equation Models.” IFAC-PapersOnLine 52 (26): 58–64.

Nousiainen, Kari, Jukka Intosalmi, and Harri Lähdesmäki. 2019. “A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation.” In Algorithms for Computational Biology, edited by Ian Holmes, Carlos Martı́n-Vide, and Miguel A. Vega-Rodrı́guez, 191–202. Cham: Springer International Publishing.

Pedretscher, B., B. Kaltenbacher, and O. Pfeiler. 2019. “Parameter Identification and Uncertainty Quantification in Stochastic State Space Models and Its Application to Texture Analysis.” Applied Numerical Mathematics 146: 38–54.

Pitt, Jake Alan, and Julio R Banga. 2019. “Parameter Estimation in Models of Biological Oscillators: An Automated Regularised Estimation Approach.” BMC Bioinformatics 20 (1): 82.

Schmiester, Leonard, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer, and Daniel Weindl. 2019. “Efficient parameterization of large-scale dynamic models based on relative measurements.” Bioinformatics, July.

Stapor, Paul, Leonard Schmiester, Christoph Wierling, Bodo Lange, Daniel Weindl, and Jan Hasenauer. 2019. “Mini-Batch Optimization Enables Training of Ode Models on Large-Scale Datasets.” bioRxiv.

Terje Lines, Glenn, Łukasz Paszkowski, Leonard Schmiester, Daniel Weindl, Paul Stapor, and Jan Hasenauer. 2019. “Efficient Computation of Steady States in Large-Scale Ode Models of Biochemical Reaction Networks.” IFAC-PapersOnLine 52 (26): 32–37.

Villaverde, Alejandro F., Elba Raimúndez, Jan Hasenauer, and Julio R. Banga. 2019. “A Comparison of Methods for Quantifying Prediction Uncertainty in Systems Biology⁎⁎This Research Has Received Funding from the European Unions Horizon 2020 Research and Innovation Program Under Grant Agreement No 686282 (Canpathpro) and the German Ministry of Education and Research (Bmbf) Under the Grant Agreement No 01ZX1310B (Sys-Stomach) and No 01ZX1705A (Income).” IFAC-PapersOnLine 52 (26): 45–51.

Wang, Dantong, Paul Stapor, and Jan Hasenauer. 2019. “Dirac Mixture Distributions for the Approximation of Mixed Effects Models.” IFAC-PapersOnLine 52 (26): 200–206.

Watanabe, Simon Berglund. 2019. “Identifiability of Parameters in Pbpk Models.” Master’s thesis, Chalmers University of Technology / Department of Mathematical Sciences.


Ballnus, Benjamin, Steffen Schaper, Fabian J Theis, and Jan Hasenauer. 2018. “Bayesian Parameter Estimation for Biochemical Reaction Networks Using Region-Based Adaptive Parallel Tempering.” Bioinformatics 34 (13): i494–i501.

Bast, Lisa, Filippo Calzolari, Michael Strasser, Jan Hasenauer, Fabian J. Theis, Jovica Ninkovic, and Carsten Marr. 2018. “Subtle Changes in Clonal Dynamics Underlie the Age-Related Decline in Neurogenesis.” Cell Reports.

Fröhlich, Fabian, Thomas Kessler, Daniel Weindl, Alexey Shadrin, Leonard Schmiester, Hendrik Hache, Artur Muradyan, et al. 2018. “Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model.” Cell Systems 7 (6): 567–579.e6.

Fröhlich, Fabian, Anita Reiser, Laura Fink, Daniel Woschée, Thomas Ligon, Fabian Joachim Theis, Joachim Oskar Rädler, and Jan Hasenauer. 2018. “Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics After Transfection.” Npj Systems Biology and Applications 5 (1): 1.

Kaltenbacher, Barbara, and Barbara Pedretscher. 2018. “Parameter Estimation in Sdes via the Fokker–Planck Equation: Likelihood Function and Adjoint Based Gradient Computation.” Journal of Mathematical Analysis and Applications 465 (2): 872–84.

Loos, Carolin, Sabrina Krause, and Jan Hasenauer. 2018. “Hierarchical Optimization for the Efficient Parametrization of ODE Models.” Bioinformatics 34 (24): 4266–73.

Loos, Carolin, Katharina Moeller, Fabian Fröhlich, Tim Hucho, and Jan Hasenauer. 2018. “A Hierarchical, Data-Driven Approach to Modeling Single-Cell Populations Predicts Latent Causes of Cell-to-Cell Variability.” Cell Systems 6 (5): 593–603.

Pitt, Jake Alan, Lucian Gomoescu, Constantinos C. Pantelides, Benoît Chachuat, and Julio R. Banga. 2018. “Critical Assessment of Parameter Estimation Methods in Models of Biological Oscillators⁎⁎This Project Has Received Funding from the European Unions Horizon 2020 Research and Innovation Program Under Grant Agreement 675585 (Marie Sklodowska-Curie Itn Symbiosys). The Authors Jap and Lg Are Marie Sklodowska-Curie Early Stage Researchers at Iim-Csic (Spain) and Pse Ltd (Uk) Respectively.” IFAC-PapersOnLine 51 (19): 72–75.

Schälte, Y., P. Stapor, and J. Hasenauer. 2018. “Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology.” FAC-PapersOnLine 51 (19): 98–101.

Stapor, Paul, Fabian Fröhlich, and Jan Hasenauer. 2018. “Optimization and Profile Calculation of ODE Models Using Second Order Adjoint Sensitivity Analysis.” Bioinformatics 34 (13): i151–i159.

Villaverde, Alejandro F, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer, and Julio R Banga. 2018. “Benchmarking optimization methods for parameter estimation in large kinetic models.” Bioinformatics 35 (5): 830–38.


Ballnus, B., S. Hug, K. Hatz, L. Görlitz, J. Hasenauer, and F. J. Theis. 2017. “Comprehensive Benchmarking of Markov Chain Monte Carlo Methods for Dynamical Systems.” BMC Syst. Biol. 11 (63).

Fröhlich, F., B. Kaltenbacher, F. J. Theis, and J. Hasenauer. 2017. “Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.” PLoS Comput. Biol. 13 (1): e1005331.

Fröhlich, F., F. J. Theis, J. O. Rädler, and J. Hasenauer. 2017. “Parameter Estimation for Dynamical Systems with Discrete Events and Logical Operations.” Bioinformatics 33 (7): 1049–56.

Kazeroonian, A., F. J. Theis, and J. Hasenauer. 2017. “A Scalable Moment-Closure Approximation for Large-Scale Biochemical Reaction Networks.” Bioinformatics 33 (14): i293–i300.

Maier, C., C. Loos, and J. Hasenauer. 2017. “Robust Parameter Estimation for Dynamical Systems from Outlier-Corrupted Data.” Bioinformatics 33 (5): 718–25.


Boiger, R., J. Hasenauer, S. Hross, and B. Kaltenbacher. 2016. “Integration Based Profile Likelihood Calculation for PDE Constrained Parameter Estimation Problems.” Inverse Prob. 32 (12): 125009.

Fiedler, A., S. Raeth, F. J. Theis, A. Hausser, and J. Hasenauer. 2016. “Tailored Parameter Optimization Methods for Ordinary Differential Equation Models with Steady-State Constraints.” BMC Syst. Biol. 10 (80).

Fröhlich, F., P. Thomas, A. Kazeroonian, F. J. Theis, R. Grima, and J. Hasenauer. 2016. “Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.” PLoS Comput. Biol. 12 (7): e1005030.

Hross, S., A. Fiedler, F. J. Theis, and J. Hasenauer. 2016. “Quantitative Comparison of Competing PDE Models for Pom1p Dynamics in Fission Yeast.” In Proc. 6th IFAC Conf. Found. Syst. Biol. Eng., edited by R. Findeisen, E. Bullinger, E. Balsa-Canto, and K. Bernaerts, 49:264–69. 26. IFAC-PapersOnLine.

Kazeroonian, A., F. Fröhlich, A. Raue, F. J. Theis, and J. Hasenauer. 2016. “CERENA: Chemical REaction Network Analyzer – A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics.” PLoS ONE 11 (1): e0146732.

Loos, C., A. Fiedler, and J. Hasenauer. 2016. “Parameter Estimation for Reaction Rate Equation Constrained Mixture Models.” In Proc. 13th Int. Conf. Comp. Meth. Syst. Biol., edited by E. Bartocci, P. Lio, and N. Paoletti, 186–200. Lecture Notes in Bioinformatics. Springer International Publishing.


Loos, C., C. Marr, F. J. Theis, and J. Hasenauer. 2015. “Computational Methods in Systems Biology.” In, edited by O. Roux and J. Bourdon, 9308:52–63. Lecture Notes in Computer Science. Springer International Publishing.