Publications

Preprints

[aa] A. F. Villaverde, D. Pathirana, F. Fröhlich, J. Hasenauer, and J. R. Banga. A protocol for dynamic model calibration. arXiv preprint arXiv:2105.12008, May 2021.

[aa] S. Pieschner, J. Hasenauer, and C. Fuchs. Identifiability analysis for models of the translation kinetics after mRNA transfection. bioRxiv, , May 2021. DOI: 10.1101/2021.05.18.444633

[aa] K. Radon, A. Bakuli, P. Pütz, R. L. Gleut, J. M. Guggenbuehl Noller, L. Olbrich, E. Saathoff, M. Gar\i, Y. Schälte, T. Frahnow, R. Wölfel, M. Pritsch, C. Rothe, M. Pletschette, R. Rubio-Acero, J. Beyerl, D. Metaxa, F. Forster, V. Thiel, N. Castelletti, F. Rieß, M. N. Diefenbach, G. Fröschl, J. Bruger, S. Winter, J. Frese, K. Puchinger, I. Brand, I. Kroidl, A. Wieser, M. Hoelscher, J. Hasenauer, C. Fuchs, and on behalf of the KoCo19 study group. From first to second wave: follow-up of the prospective Covid-19 cohort (KoCo19) in Munich (Germany). medRxiv, April 2021. DOI: 10.1101/2021.04.27.21256133

[aa] L. Olbrich,  N. Castelletti,  Y. Schälte,  M. Garí,  P. Pütz,  A. Bakuli,  M. Pritsch,  I. Kroidl,  E. Saathoff,  J. M. G. Noller,  V. Fingerle,  R. Le Gleut,  L. Gilberg,  I. Brand,  P. Falk,  A. Markgraf,  F. Deák,  F. Riess,  M. Diefenbach,  T. Eser,  F. Weinauer,  S. Martin,  E.-M. Quenzel,  M. Becker,  J. Durner,  P. Girl,  K. Müller,  K. Radon,  C. Fuchs,  R. Wölfel,  J. Hasenauer,  M. Hoelscher, and  A. Wieser. A Serology Strategy for Epidemiological Studies Based on the Comparison of the Performance of Seven Different Test Systems - The Representative COVID-19 Cohort Munich. medRxiv, January 2021. DOI: 10.1101/2021.01.13.21249735  

Journal Publications

[81] J. Vanhoefer*, M. R. a. Matos, D. Pathirana, Y. Schälte, and J. Hasenauer. yaml2sbml: Human-readable and -writable specification of ODE models and their conversion to SBML. Journal of Open Source Software, 6(61):3215, May 2021. DOI: 10.21105/joss.03215

[80] W. Fujii, T. S. Kapellos, K. Baßler, K. Händler, L. Holsten, R. Knoll, S. Warnat-Herresthal, M. Oestreich, E. R. Hinkley, J. Hasenauer, C. Pizarro, C. Thiele, A. C. Aschenbrenner, T. Ulas, D. Skowasch, and J. L. Schultze. Alveolar macrophage transcriptomic profiling in COPD shows major lipid metabolism changes. ERJ Open Research, , May 2021. DOI: 10.1183/23120541.00915-2020

[79] I. Haffner, K. Schierle, E. Raimúndez, B. Geier, D. Maier, J. Hasenauer, B. Luber, A. Walch, K. Kolbe, J. Riera Knorrenschild, A. Kretzschmar, B. Rau, L. Fischer von Weikersthal, M. Ahlborn, G. Siegler, S. Fuxius, T. Decker, C. Wittekind, and F. Lordick. HER2 Expression, Test Deviations, and Their Impact on Survival in Metastatic Gastric Cancer: Results From the Prospective Multicenter VARIANZ Study. Journal of Clinical Oncology, 39(13):1468-1478, May 2021. DOI: 10.1200/JCO.20.02761

[78] F. Fröhlich, D. Weindl, Y. Schälte, D. Pathirana, Ł. Paszkowski, G. T. Lines, P. Stapor, and J. Hasenauer. AMICI: high-performance sensitivity analysis for large ordinary differential equation models. Bioinformatics, btab227, April 2021. DOI: 10.1093/bioinformatics/btab227

[77] M. Pritsch, K. Radon, A. Bakuli, R. Le Gleut, L. Olbrich, J. M. Guggenbüehl Noller, E. Saathoff, N. Castelletti, M. Garí, P. Pütz, Y. Schälte, T. Frahnow, R. Wölfel, C. Rothe, M. Pletschette, D. Metaxa, F. Forster, V. Thiel, F. Rieß, M. N. Diefenbach, G. Fröschl, J. Bruger, S. Winter, J. Frese, K. Puchinger, I. Brand, I. Kroidl, J. Hasenauer, C. Fuchs, A. Wieser, M. Hoelscher, and on behalf of the KoCo19 study group. Prevalence and Risk Factors of Infection in the Representative COVID-19 Cohort Munich. International Journal of Environmental Research and Public Health, 18(7), March 2021. DOI: 10.3390/ijerph18073572

[76] E. Raimúndez, E. Dudkin, J. Vanhoefer, E. Alamoudi, S. Merkt, L. Fuhrmann, F. Bai, and J. Hasenauer. COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling. Epidemics, 34:100439, March 2021. DOI: https://doi.org/10.1016/j.epidem.2021.100439

[75] 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(1):2696, January 2021. DOI: 10.1038/s41598-021-82196-2

[74] L. Schmiester*, Y. Schälte*, F. T. Bergmann, T. Camba, E. Dudkin, J. Egert, F. Fröhlich, L. Fuhrmann, A. L. Hauber, S. Kemmer, P. Lakrisenko, C. Loos, S. Merkt, W. Müller, D. Pathirana, E. Raimúndez, L. Refisch, M. Rosenblatt, P. L. Stapor, P. Städter, D. Wang, F.-G. Wieland, J. R. Banga, J. Timmer, A. F. Villaverde, S. Sahle, C. Kreutz, J. Hasenauer**, and D. Weindl**. PEtab---Interoperable specification of parameter estimation problems in systems biology. PLOS Computational Biology, 17(1):1-10, January 2021. DOI: 10.1371/journal.pcbi.1008646

2020

[73] C. Alabert, C. Loos, M. Voelker-Albert, S. Graziano, I. Forné, N. Reveron-Gomez, L. Schuh, J. Hasenauer, C. Marr, A. Imhof, and A. Groth. Domain Model Explains Propagation Dynamics and Stability of Histone H3K27 and H3K36 Methylation Landscapes. Cell Reports, 30(4):1223-1234.e8, January 2020. doi: 10.1016/j.celrep.2019.12.060.

[72] K. Bassler, W. Fujii, T. S. Kapellos, A. Horne, B. Reiz, E. Dudkin, M. Lücken, N. Reusch, C. Osei-Sarpong, S. Warnat-Herresthal, A. Wagner, L. Bonaguro, P. Günther, C. Pizarro, T. Schreiber, M. Becker, K. Händler, C. T. Wohnhaas, F. Baumgartner, M. Köhler, H. Theis, M. Kraut, M. H. Wadsworth, T. K. Hughes, H. J. G. Ferreira, J. Schulte-Schrepping, E. Hinkley, I. H. Kaltheuner, M. Geyer, C. Thiele, A. K. Shalek, A. Feißt, D. Thomas, H. Dickten, M. Beyer, P. Baum, N. Yosef, A. C. Aschenbrenner, T. Ulas, J. Hasenauer, F. J. Theis, D. Skowasch, and J. L. Schultze. Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease. bioRxiv, 2020. doi: 10.1101/2020.05.28.121541.

[71] K. Ebert, G. Zwingenberger, E. Barbaria, S. Keller, C. Heck, R. Arnold, V. Hollerieth, J. Mattes, R. Geffers, E. Raimúndez, J. Hasenauer, and B. Luber. Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines. BMC Cancer, 20(1):1039, October 2020. doi: 10.1186/s12885-020-07540-7.

[70] D. Hoksza, P. Gawron, M. Ostaszewski, J. Hasenauer, and R. Schneider. Closing the gap between formats for storing layout information in systems biology. Briefings in Bioinformatics, 21(4):1249–1260, July 2020. doi: 10.1093/bib/bbz067.

[69] C. Loos and J. Hasenauer. Robust calibration of hierarchical population models for heterogeneous cell populations. Journal of Theoretical Biology, 488(110118), March 2020. doi: 10.1016/j.jtbi.2019.110118.

[68] M. Ostaszewski, A. Mazein, M. E. Gillespie, I. Kuperstein, A. Niarakis, H. Hermjakob, A. R. Pico, E. L. Willighagen, C. T. Evelo, J. Hasenauer, F. Schreiber, A. Dräger, E. Demir, O. Wolkenhauer, L. I. Furlong, E. Barillot, J. Dopazo, A. Orta-Resendiz, F. Messina, A. Valencia, A. Funahashi, H. Kitano, C. Auffray, R. Balling, and R. Schneider. COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms. Scientific Data, 7(1):136, 2020. doi: 10.1038/s41597-020-0477-8.

[67] K. Radon, E. Saathoff, M. Pritsch, J. M. G. Noller, I. Kroidl, L. Olbrich, V. Thiel, M. Diefenbach, F. Riess, F. Forster, F. Theis, A. Wieser, M. Hoelscher, A. Bakuli, J. Eckstein, G. Froeschl, O. Geisenberger, C. Geldmacher, A. Heiber, L. Hoffmann, K. Huber, D. Metaxa, M. Pletschette, C. Rothe, M. Schunk, C. Wallrauch, T. Zimmer, S. Prückner, J. Hasenauer, N. Castelletti, E. Zeggini, M. Laxy, R. Leidl, and L. Schwettmann. Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19). BMC Public Health, 20(1):1036, June 2020. doi: 10.1186/s12889-020-09164-9.

[66] E. Raimúndez, S. Keller, G. Zwingenberger, K. Ebert, S. Hug, F. J. Theis, D. Maier, B. Luber, and J. Hasenauer. Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines. PLoS Comput. Biol., 16(3):e1007147, 2020.

[65] Y. Schälte and J. Hasenauer. Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation. Bioinformatics, 36(Supplement 1):i551–i559, July 2020. doi: 10.1093/bioinformatics/btaa397.

[64] L. Schmiester, Y. Schälte, F. Fröhlich, J. Hasenauer, and D. Weindl. Efficient parameterization of large-scale dynamic models based on relative measurements. Bioinformatics, 36(2):594–602, January 2020. doi: 10.1093/bioinformatics/btz581.

[63] L. Schmiester, D. Weindl, and J. Hasenauer. Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach. J. Math. Biol., 81(2):603–623, July 2020. doi: 10.1007/s00285-020-01522-w.

2019

[62] L. Schmiester*, Y. Schälte*, F. Fröhlich, J. Hasenauer**, and D. Weindl**. Efficient parameterization of large-scale dynamic models based on relative measurements. Bioinformatics, July 2019. DOI: 10.1093/bioinformatics/btz581

[61] D. Hoksza, P. Gawron, M. Ostaszewski, J. Hasenauer, and R. Schneider. Closing the gap between formats for storing layout information in systems biology. Briefings in Bioinformatics, bbz067, (2019). https://doi.org/10.1093/bib/bbz067/

[60] E.-M. Geissen, J. Hasenauer, and N. E. Radde. Inference of finite mixture models and the effect of binning. Statistical Applications in Genetics and Molecular Biology, 2019. https://doi.org/10.1515/sagmb-2018-0035

[59] D. S. Fischer, A. K. Fiedler, E. M. Kernfeld, R. M. J. Genga, A. Bastidas-Ponce, M. Bakhti, H. Lickert, J. Hasenauer, R. Maehr, and F. J. Theis Inferring population dynamics from single-cell RNA-sequencing time series data. Nature Biotechnology, 37, 461-468, April 2019.

[58] M. Sinzger, J. Vanhoefer, C. Loos, and J. Hasenauer. Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible. Scientic Reports, 9:3002, January 2019.

[57] H. Hass*, C. Loos*, E. Raimundez Alvarez, J. Timmer, J. Hasenauer**, and C. Kreutz** . Benchmark problems for dynamic modeling of intracellular processes. Bioinformatics, January 2019. DOI: 10.1093/bioinformatics/btz020.

2018

[56] S. Hross, F.J. Theis, M. Sixt, and J. Hasenauer. Mechanistic description of spatial processes using integrative
modelling of noise-corrupted imaging data.  Journal of The Royal Society Interface, 15(149):20180600,
December 2018.

[55] F. Fröhlich*, T. Kessler*, D. Weindl, A. Shadrin, L. Schmiester, H. Hache, A. Muradyan, M. Schütte, J.-H. Lim,
M. Heinig, F. J. Theis, H. Lehrach, C. Wierling, B. Lange**, and J. Hasenauer**. Efficient parameter estimation
enables the prediction of drug response using a mechanistic pan-cancer pathway model.  Cell Systems, 7(6):567-
579.e6, December 2018.

[54] F. Fröhlich, A. Reiser, L. Fink, D. Woschée, T. Ligon, F. J. Theis, J. O. Rädler, and J. Hasenauer. Multi-experiment
nonlinear mixed effect modeling of single-cell translation kinetics after transfection.  npj Systems
Biology and Applications, October 2018. DOI: 10.1038/s41540-018-0079-7.

[53] L. Bast, F. Calzolari, M. Strasser, J. Hasenauer, F. J. Theis, J. Ninkovic, and C. Marr. Subtle changes in clonal
dynamics underlie the age-related decline in neurogenesis. accepted for publication in  Cell Reports, September 2018.

[52] A. F. Villaverde, F. Fröhlich, D. Weindl, J. Hasenauer**, and J. R. Banga**. Benchmarking optimization
methods for parameter estimation in large kinetic models.  Bioinformatics, August 2018. DOI: 10.1093/bioinformatics/
bty736.

[51] C. Loos*, S. Krause*, and J. Hasenauer. Hierarchical optimization for the efficient parametrization of ODE
models.  Bioinformatics, 34(24):4266-4273, December 2018.

[50] E. Klinger, D. Rickert, and J. Hasenauer. pyABC: distributed, likelihood-free inference.  Bioinformatics,
34(20):3591-3593, October 2018.

[49] M. Ostaszewski, S. Gebel, I. Kuperstein, A. Mazein, A. Zinovyev, U. Dogrusoz, J. Hasenauer, R. Fleming,
N. Le Novere, P. Gawron, T. Ligon, A. Niarakis, D. Nickerson, D. Weindl, R. Balling, E. Barillot, C. Auffray,
and R. Schneider. Community-driven roadmap for integrated disease maps.  Briefings in Bioinformatics, March
2018. DOI: 10.1093/bib/bby024.

[48] P. Stapor, F. Fröhlich, and J. Hasenauer. Optimization and profile calculation of ODE models using second
order adjoint sensitivity analysis.  Bioinformatics, 34(13):i151-i159, July 2018.

[47] B. Ballnus, S. Schaper, F. J. Theis, and J. Hasenauer. Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering.  Bioinformatics, 34(13):i494-i501, July 2018.

[46] C. Loos, K. Moeller, F. Fröhlich, T. Hucho, and J. Hasenauer. A hierarchical, data-driven approach to
modeling single-cell populations predicts latent causes of cell-to-cell variability.  Cell Systems, 6(5)593-603.e13,
May 2018.

[45] J. Isensee, M. Kaufholz, M. Knape, J. Hasenauer, H. Hammerich, H. Gonczarowska-Jorge, R. Zahedi, F. Schwede,
F. Herberg, and T. Hucho. PKA-RII subunit phosphorylation precedes activation by cAMP and regulates
activity termination.  Journal of Cell Biology, 217(6):2167-2184, June 2018.

[44] S. Keller, G. Zwingenberger, K. Ebert, J. Hasenauer, J. Wasmuth, D. Maier, and B. Luber. Effects of
trastuzumab and afatinib on kinase activity in gastric cancer cell lines.  Molecular Oncology, 12(4):441-462,
April 2018.

[43] T. S. Ligon, F. Fröhlich, O. T. Chis, J. R. Banga, E. Balsa-Canto, and J. Hasenauer. GenSSI 2.0: Multiexperiment
structural identifiability analysis of SBML models. Bioinformatics, 34(8):1421-1423, April 2018.

[42] M. Rehm, R. Apweiler, T. Beissbarth, M. Berthold, N. Blüthgen, Y. Burmeister, O. Dammann, A. Deutsch,
F. Feuerhake, A. Franke, J. Hasenauer, S. Hoffmann, T. Höfer, P. Jansen, L. Kaderali, U. Klingmüller, I. Koch,
O. Kohlbacher, L. Küpfer, F. Lammert, D. Maier, N. Pfeifer, N. Radde, I. Röder, J. Saez-Rodriguez, U. Sax,
B. Schmeck, A. Schuppert, B. Seilheimer, F. Theis, J. Vera-Gonzàles, and O. Wolkenhauer. Whither systems
medicine?  Experimental & Molecular Medicine, 50(3):e453, March 2018.

[41] P. Stapor, D. Weindl, B. Ballnus, S, Hug, C. Loos, A. Fiedler, S. Krause, S. Hross, F. Fröhlich, and J. Hase-
nauer. PESTO: Parameter EStimation TOolbox.  Bioinformatics, 34(4):705-707, February 2018.

2017

[40] S. Keller, J. Kneissl, V. Grabher-Meier, S. Heindl, J. Hasenauer, D. Maier, J. Mattes, P. Winter, and B. Luber.
Evaluation of epidermal growth factor receptor signaling effects in gastric cancer cell lines by detailed motility-focused
phenotypic characterization linked with molecular analysis.  BMC Cancer, 17(1):845, December 2017.

[39] A. Kazeroonian, F. J. Theis, and J. Hasenauer. A scalable moment-closure approximation for large-scale
biochemical reaction networks.  Bioinformatics, 33(14):i293-i300, July 2017.

[38] B. Ballnus, S. Hug, K. Hatz, L. Görlitz, J. Hasenauer, and F. J. Theis. Comprehensive benchmarking of
Markov chain Monte Carlo methods for dynamical systems.  BMC Systems Biology, 11:63, June 2017.

[37] F. Fröhlich, F. J. Theis, J. Rädler, and J. Hasenauer. Parameter estimation for dynamical systems with discrete
events and logical operations.  Bioinformatics, 33(7):1049-1056, April 2017.

[36] C. Maier, C. Loos, and J. Hasenauer. Robust parameter estimation for dynamical systems from outlier-corrupted
data.  Bioinformatics, 33(5):718-725, March 2017.

[35] N. Jagiella, D. Rickert, F. J. Theis, and J. Hasenauer. Parallelization and high-performance computing enables
automated statistical inference of multiscale models.  Cell Systems, 4(2):194-206, February 2017.

[34] F. Fröhlich, B. Kaltenbacher, F. J. Theis, and J. Hasenauer. Scalable parameter estimation for genome-scale
biochemical reaction networks.  PLoS Computational Biology, 13(1):e1005331, January 2017.

2016

[33] S. Ebinger, S. Z. Özdemir, C. Ziegenhain, S. Tiedt, C. C. Alves, M. Grunert, M. Dworzak, C. Lutz, V. A. Turati,
T. Enver, H.-P. Horny, K. Sotlar, S. Parekh, K. Spiekermann, W. Hiddemann, A. Schepers, B. Polzer, S. Kirsch,
M. Hoffmann, B. Knapp, J. Hasenauer, H. Pfeifer, R. Panzer-Grümayer, W. Enard, O. Gires, I. Jeremias.
Characterization of rare, dormant, and therapy-resistant cells in acute lymphoblastic leukemia.  Cancer Cell,
30(6):849-862, December 2016.

[32] R. Boiger, J. Hasenauer, S. Hross, and B. Kaltenbacher. Integration based profile likelihood calculation for
PDE constrained optimization problems.  Inverse Problems, 2016, 32(12):125009, December 2016.

[31] S. Hross and J. Hasenauer. Analysis of CFSE time-series data using division-, age- and label-structured
population models.  Bioinformatics, 32(15): 2321-2329, August 2016.

[30] E.-M. Geissen, J. Hasenauer, S. Heinrich, S. Hauf, F. J. Theis, and N. Radde. MEMO - Multi-experiment
mixture model analysis of censored data.  Bioinformatics, 32(16):2464-2472, August 2016.

[29] F. Fröhlich, P. Thomas, A. Kazeroonian, F. J. Theis, R. Grima, and J. Hasenauer. Inference for stochastic
chemical kinetics using moment equations and system size expansion. PLoS Computational Biology,
12(7):e1005030, July 2016.

[28] A. Fiedler, S. Raeth, F. J. Theis, A. Hausser, and J. Hasenauer. Tailored parameter optimization methods for
ordinary differential equation models with steady-state constraints. BMC Systems Biology, 10:80, July 2016.

[27] S. Hug, M. Schwarzfischer, J. Hasenauer, C. Marr, and F. J. Theis. An adaptive scheduling scheme for
calculating Bayes factors with thermodynamic integration using Simpson's rule.  Statistics and Computing,
26(3):663-677, May 2016.

[26] A. Kazeroonian, F. Fröhlich, A. Raue, F. J. Theis, and J. Hasenauer. CERENA: ChEmical REaction Network
Analyzer - A toolbox for the simulation and analysis of stochastic chemical kinetics.  PLoS ONE, 11(1):e0146732,
January 2016.

[25] T. Blasi, C. Feller, J. Feigelman, J. Hasenauer, A. Imhof, F. J. Theis, P. B. Becker, and C. Marr. Combinatorial
histone acetylation patterns are generated by motif-specific reactions.  Cell Systems, 2(1):49-58, January 2016.

2015

[24] A. Filipczyk, C. Marr, S. Hastreiter, J. Feigelman, M. Schwarzfischer, P. S. Hoppe, D. Loeffler, K. D. Kokkaliaris,
M. Endele, B. Schauberger, O. Hilsenbeck, S. Skylaki, J. Hasenauer, K. Anastassiadis, F. J. Theis, and
T. Schroeder. Network plasticity of pluripotency transcription factors in embryonic stem cells.  Nature Cell
Biology, 17:1235-1246, October 2015.

[23] J. Hasenauer, N. Jagiella, S. Hross, and F. J. Theis. Data-driven modelling of biological multi-scale processes.
Journal of Coupled Systems and Multiscale Dynamics, 3(2):101-121, June 2015.

[22] A. Raue, B. Steiert, M. Schelker, C. Kreutz, T. Maiwald, H. Hass, J. Vanlier, C. Tönsing, L. Adlung, R. Engesser,
W. Mader, T. Heinemann, J. Hasenauer, M. Schilling, T. Höfer, E. Klipp, F. J. Theis, U. Klingmüller,
B. Schöberl, and J. Timmer. Data2Dynamics: a modeling environment tailored to parameter estimation in
dynamical systems.  Bioinformatics, 31(21):3558-3560, November 2015.

2014

[21] M. Löhning, M. Reble, J. Hasenauer, S. Yua, and F. Allgöwer. Model predictive control using reduced order
models: guaranteed stability for constrained linear systems.  Journal of Process Control, 24(11): 1647-1659,
November 2014.
Awarded with the Journal of Process Control Paper Prize Award

[20] J. Isensee, M. Diskar, S. Waldherr, R. Buschow, J. Hasenauer, A. Prinz, F. Allgöwer, F. W. Herberg, and
T. Hucho. Pain modulators regulate the dynamics of PKA-RII phosphorylation in subgroups of sensory neurons. 
Journal of Cell Science, 127:216-229, September 2014.
Selected as cover story

[19] J. Hasenauer, V. Wolf, A. Kazeroonian, and F. J. Theis. Method of conditional moments (MCM) for the
chemical master equation.  Journal of Mathematical Biology, 69(3):687-735, August 2014.

[18] J. Hasenauer, C. Hasenauer, T. Hucho, and F. J. Theis. ODE constrained mixture modelling: A method for
unraveling subpopulation structures and dynamics.  PLoS Computational Biology, 10(7):e1003686, July 2014.

[17] S. Neumann, J. Hasenauer, N. Pollak, and P. Scheurich. Dominant negative effects of Tumor Necrosis Factor
(TNF)-related apoptosis-inducing ligand (TRAIL) receptor 4 on TRAIL receptor 1 signaling by formation of
heteromeric complexes.  Journal of Biological Chemistry, 289(23):16576-16587, June 2014.

2013

[16] S. Heinrich, E.-M. Geissen, S. Trautmann, J. Kamenz, C. Widmer, P. Drewe, M. Knop, N. Radde, J. Hasenauer,
and S. Hauf. Determinants of robustness in spindle assembly checkpoint signalling.  Nature Cell Biology,
15(11):1328-1339, November 2013.
Selected as cover story

[15] C. Vehlow*, J. Hasenauer*, A. Kramer, A. Raue, S. Hug, J. Timmer, N. Radde, F. J. Theis, and D. Weiskopf.
iVUN: interactive visualization of uncertain biochemical reaction networks.  BMC Systems Biology, 14 (Suppl
19):S2, November 2013.

[14] C. Andres, J. Hasenauer, H.-S. Ahn, E. K. Joseph, J. Isensee, F. J. Theis, F. Allgöwer, J. D. Levine, S. D. Dib-
Hajj, S. G. Waxman, and T. Hucho. Wound healing growth factor, basic FGF, induces Erk1/2 dependent
mechanical hyperalgesia.  Pain, 154(10):2216-2226, October 2013.

[13] S. Hock, J. Hasenauer, and F. J. Theis. Modeling of 2D diffusion processes based on microscopy data:
Parameter estimation and practical identifiability analysis.  BMC Bioinformatics, 14 (Suppl 10):S7, August 2013.

[12] S. Hock, Y.-K. Ng, J. Hasenauer, D. Wittmann, D. Lutter, D. Trumbach, W. Wurst, N. Prakash, and F. J.
Theis. Sharpening of expression domains induced by transcription and microRNA regulation within a spatiotemporal
model of mid-hindbrain boundary formation.  BMC Systems Biology, 7:48, July 2013.

[11] S. Hug, A. Raue, J. Hasenauer, J. Bachmann, U. Klingmüller, J. Timmer, and F. J. Theis. High-dimensional
Bayesian parameter estimation: Case study for a model of JAK2/STAT5 signaling.  Mathematical Biosciences,
246(2):293-304, April 2013.

2012

[10] J. Hasenauer, D. Schittler, and F. Allgöwer. Analysis and simulation of division- and label-structured population
models: A new tool to analyze proliferation assays.  Bulletin of Mathematical Biology, 74(11):2692-2732,
November 2012.

[9] J. Hasenauer, J. Heinrich, M. Doszczak, P. Scheurich, D. Weiskopf, and F. Allgöwer. A visual analytics
approach for models of heterogeneous cell populations.  EURASIP Journal on Bioinformatics and Systems
Biology, 4, May 2012.

[8] J. Hasenauer, M. Löhning, M. Khammash, and F. Allgöwer. Dynamical optimization using reduced order
models: A method to guarantee performance.  Journal of Process Control, 22(8):1490-1501, September 2012.

[7] C. Andres*, J. Hasenauer*, F. Allgöwer, and T. Hucho. Threshold-free population analysis identifies medium-sized
DRG-neurons to respond strongest to NGF stimulation.  PLoS ONE, 7(3):e34257, March 2012.

2011

[6] J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich, and F. Allgöwer. Analysis of heterogeneous
cell populations: A density-based modeling and identification framework.  Journal of Process Control,
21(10):1417-1425, December 2011.

[5] J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich, and F. Allgöwer. Identification of models
of heterogeneous cell populations from population snapshot data.  BMC Bioinformatics, 12:125, April 2011.

2010

[4] D. Schittler, J. Hasenauer, F. Allgöwer, and S. Waldherr. Cell differentiation modeled via a coupled two-switch
regulatory network.  Chaos, 20(4):045121, December 2010.

[3] J. Hasenauer*, P. Rumschinski*, S. Waldherr, S. Borchers, F. Allgöwer, and R. Findeisen. Guaranteed steady
state bounds for uncertain (bio-)chemical processes using infeasibility certificates.  Journal of Process Control,
20(9):1076-1083, October 2010.

[2] J. Hasenauer, S. Waldherr, N. Radde, M. Doszczak, P. Scheurich, and F. Allgöwer. A maximum likelihood
estimator for parameter distributions in heterogeneous cell populations.  Procedia Computer Science, 1(1):1649-
1657, May 2010.

[1] J. Hasenauer*, S. Waldherr, K. Wagner, and F. Allgöwer. Parameter identification, experimental design and model
falsification for biological network models using semidefinite programming.  IET Systems Biology, 4(2):119-
130, March 2010.

Peer-Reviewed Conference Publications and Book Chapters

2019

[35] A. F. Villaverde, E. Raimundez, J. Hasenauer, and J. R. Banga. A Comparison of methods for Quantifying Prediction Uncertainty in Systems Biology. accepted for Proceedings of the 8th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE), Valencia, Spain, October 2019.

[34] G. T. Lines, L. Paszkowski, L. Schmiester, D. Weindl, P. Stapor, and J. Hasenauer. Efficient computation of steady states in large-scale ODE models of biochemical reaction networks. accepted for Proceedings of the 8th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE), Valencia, Spain, October 2019.

[33] E. M. Kapfer, P. Stapor, and J. Hasenauer. Challenges in the calibration of large-scale ordinary differential equation models. accepted for Proceedings of the 8th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE), Valencia, Spain, October 2019.

[32] D. Wang, P. Stapor, and J. Hasenauer. Dirac mixture distributions for the approximation of mixed effects models. accepted for Proceedings of the 8th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE), Valencia, Spain, October 2019.

[31] F. Fröhlich, C. Loos, and J. Hasenauer. Scalable inference of ordinary differential equation models of biochemical processes. In Gene Regulatory Networks, volume 1883 of Methods in Molecular Biology, pages 385-422. Humana Press, New York, NY, January 2019.

2018

[30] J. Feigelman, D. Weindl, F. J. Theis, C. Marr, and J. Hasenauer. LNA++: Linear Noise Approximation with
first and second order sensitivities. In  Computational Methods in Systems Biology, volume 11095 of  Lecture
Notes in Computer Science, pages 300-306. Springer International Publishing, September 2018.

[29] Y. Schälte, P. Stapor, and J. Hasenauer. Evaluation of derivative-free optimizers for parameter estimation in
systems biology. In  Proceedings of the 7th IFAC Conference on Foundations of Systems Biology in Engineering
(FOSBE), Chicago, Illinous, USA, August 2018.

2017

[28] E. Klinger, and J. Hasenauer. A scheme for adaptive selection of population sizes in Approximate Bayesian
Computation - Sequential Monte Carlo. In  Computational Methods in Systems Biology, volume 10545 of
Lecture Notes in Computer Science, pages 128-144. Springer International Publishing, September 2017.

2016

[27] S. Hross, A. Fiedler, and J. Hasenauer. Quantitative comparison of competing PDE models for Pom1p
dynamics in fission yeast. In  Proceedings of the 6th IFAC Conference on Foundations of Systems Biology in
Engineering (FOSBE), Magdeburg, Germany, pages 264-269, October 2016.

[26] C. Loos, A. Fiedler, and J. Hasenauer. Parameter estimation for reaction rate equation constrained mixture
models. In  Computational Methods in Systems Biology, volume 9859 of  Lecture Notes in Computer Science,
pages 186-200. Springer International Publishing, September 2016.

2015

[25] C. Loos, C. Marr, F. J. Theis, and J. Hasenauer. Approximate Bayesian Computation for stochastic single-cell
time-lapse data using multivariate test statistics. In  Computational Methods in Systems Biology, volume 9308
of  Lecture Notes in Computer Science, pages 52-63. Springer International Publishing, September 2015.

2014

[24] F. Fröhlich, F. J. Theis, and J. Hasenauer. Uncertainty analysis for non-identifiable dynamical systems: Profile
likelihoods, bootstrapping and more. In  Computational Methods in Systems Biology, volume 8859 of  Lecture
Notes in Computer Science, pages 61-72. Springer International Publishing, November 2014.

[23] F. Fröhlich, S. Hross, F. J. Theis, and J. Hasenauer. Radial basis function approximation of Bayesian parameter
posterior densities for uncertainty analysis. In  Computational Methods in Systems Biology, volume 8859 of
Lecture Notes in Computer Science, pages 73-85. Springer International Publishing, November 2014.

[22] A.Kazeroonian, F. J. Theis, and J. Hasenauer. Modeling of stochastic biological processes with non-polynomial
propensities using non-central conditional moment equation. In  Proceedings of the 19th IFAC World Congress,
Cape Town, South Africa, pages 1729-1735, August 2014.

2013

[21] A. Kazeroonian, J. Hasenauer, and F. J. Theis. Parameter estimation for stochastic biochemical processes: A
comparison of moment equation and finite state projection. In  Proceedings of the 10th International Workshop
on Computational Systems Biology (WCSB), Tampere, Finland, pages 66-73, June 2013.

[20] C. Vehlow, J. Hasenauer, F. Theis, and D. Weiskopf. Visualizing edge-edge relations in graphs. In  Proceedings
of the IEEE Pacific Visualization Symposium, Sydney, Australia, February 2013.

[19] F. Allgöwer, J. Hasenauer, and S. Waldherr. In  Encyclopedia of Systems Biology, chapter Model Falsification,
Semidefinite Programming. pages 1391-1395. Springer New York, NY, 2013.

[18] F. Allgöwer, J. Hasenauer, and S. Waldherr. In  Encyclopedia of Systems Biology, chapter Feasibility. pages
736-737. Springer New York, NY, 2013.

[17] F. Allgöwer, J. Hasenauer, and S. Waldherr. In  Encyclopedia of Systems Biology, chapter Convex optimization.
pages 501-502. Springer New York, NY, 2013.

[16] F. Allgöwer, J. Hasenauer, and S. Waldherr. In  Encyclopedia of Systems Biology, chapter Quadratic decomposition.
page 1803. Springer New York, NY, 2013.

2012

[15] C. Vehlow, J. Hasenauer, A. Kramer, J. Heinrich, N. Radde, F. Allgöwer, and D. Weiskopf. Uncertainty-aware
visual analysis of biochemical reaction networks. In  Proceedings of the IEEE Symposium on Biological Data
Visualization, Seattle, USA, pages 91-98, October 2012.

[14] D. Schittler, J. Hasenauer, and F. Allgöwer. A model for proliferating cell populations that accounts for cell
types. In  Proceedings of the 9th International Workshop on Computational Systems Biology (WCSB), Ulm,
Germany, pages 79-82, June 2012.
Awarded with the Best Student Paper Award

[13] P. Metzger, J. Hasenauer, and F. Allgöwer. Modeling and analysis of division-, age-, and label-structured cell
populations. In  Proceedings of the 9th International Workshop on Computational Systems Biology (WCSB),
Ulm, Germany, pages 55-58, June 2012.

[12] S. Waldherr, J. Hasenauer, and F. Allgöwer. Set based uncertainty analysis and parameter estimation for biological
networks with the bioSDP Toolbox. In  Proceedings of the 9th International Workshop on Computational
Systems Biology (WCSB), Ulm, Germany, pages 91-94, June 2012.

2011

[11] M. Löhning, J. Hasenauer, and F. Allgöwer. Steady state stability preserving nonlinear model reduction using
sequential convex optimization. In  Proceedings of the 50th IEEE Conference on Decision and Control (CDC),
Orlando, Florida, USA, pages 7158-7163, December 2011.

[10] P. Weber*, J. Hasenauer*, F. Allgöwer, and N. Radde. Parameter estimation and identificability of biological
networks using relative data. In  Proceedings of the 18th IFAC World Congress (IFAC), Milano, Italy, pages
11648-11653, July 2011.

[9] M. Löhning, J. Hasenauer, and F. Allgöwer. Trajectory-based model reduction of nonlinear biochemical
networks employing the observability normal form. In  Proceedings of the 18th IFAC World Congress (IFAC),
Milano, Italy, pages 10442-10447, July 2011.

[8] J. Hasenauer, J. Heinrich, M. Doszczak, P. Scheurich, D.Weiskopf, and F. Allgöwer. Visualization methods and
support vector machines as tools for determining markers in models of heterogeneous populations: Proapoptotic
signaling as a case study. In  Proceedings of the 8th International Workshop on Computational Systems Biology
(WCSB), Zürich, Switzerland, pages 61-64, June 2011.
Awarded with the Best Student Paper Award

[7] D. Schittler*, J. Hasenauer* and F. Allgöwer. A generalized population model for cell proliferation: Integrating
division numbers and label dynamics. In  Proceedings of the 8th International Workshop on Computational
Systems Biology (WCSB), Zürich, Switzerland, pages 165-168, June 2011.

[6] S. Waldherr, J. Hasenauer, M. Doszczak, P. Scheurich, and F. Allgöwer. Global uncertainty analysis for a
model of TNF-induced NF-kB signalling. In  Advances in the Theory of Control, Signals and Systems with
Physical Modeling, volume 407 of  Lecture Notes in Control and Information Sciences, pages 365-377. Springer
Berlin / Heidelberg, 2011.

2010

[5] J. Hasenauer, C. Breindl, S. Waldherr, and F. Allgöwer. Approximative classification of regions in parameter
spaces of nonlinear ODEs yielding different qualitative behavior. In  Proceedings of the 49th IEEE Conference
on Decision and Control (CDC), Atlanta, Georgia, USA, pages 4114-4119, December 2010.

[4] A. Kramer, J. Hasenauer, F. Allgöwer, and N. Radde. Computation of the posterior entropy in a Bayesian
framework for parameter estimation in biological networks. In  Proceedings of the IEEE Multi-Conference on
Systems and Control (MCS), Yokohama, Japan, pages 493-498, September 2010.

[3] J. Hasenauer, S. Waldherr, M. Doszczak, P. Scheurich, and F. Allgöwer. Density-based modeling and
identification of biochemical networks in cell populations. In  Proceedings of the 9th International Symposium on
Dynamics and Control of Process Systems (DYCOPS), Leuven, Belgium, pages 306-311, July 2010.

2009

[2] J. Hasenauer*, P. Rumschinski*, S. Waldherr, S. Borchers, F. Allgöwer, and R. Findeisen. Guaranteed steadystate
bounds for uncertain chemical processes. In  Proceedings of the International Symposium on Advanced
Control of Chemical Processes (ADCHEM), Istanbul, Turkey, pages 674-679, July 2009.

[1] S. Waldherr*, J. Hasenauer*, and F. Allgöwer. Estimation of biochemical network parameter distributions in
cell populations. In  Proceedings of the 15th IFAC Symposium on Systems Identification (SYSID), Saint-Malo,
France, volume 15, pages 1265-1270, March 2009.

Other publications

2015

[1] C. Marr, J. Hasenauer, and F. J. Theis. Models and methods for systems biology and systems medicine:
The Institute of Computational Biology at the Helmholtz Zentrum München. Systembiologie.de, 9:40-43, July
2015.

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*  shared first authors
** shared corresponding authors