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1.
Boiger, R.* ; Fiedler, A. ; Hasenauer, J. & Kaltenbacher, B.*: Continuous analogue to iterative optimization for PDE-constrained inverse problems. Inverse Prob. Sci. Eng. 27, 710-734 (2019)
2.
Fischer, D.S. et al.: Inferring population dynamics from single-cell RNA-sequencing time series data. Nat. Biotechnol. 37, 461-468 (2019)
3.
Fröhlich, F. ; Loos, C. & Hasenauer, J.: Scalable inference of ordinary differential equation models of biochemical processes. Methods Mol. Biol. 1883, 385-422 (2019)
4.
Fröhlich, F. et al.: Publisher Correction: Multi-experiment nonlinear mixed effectmodeling of single-cell translation kinetics after transfection. NPJ Syst. Biol. Appl. 5:11 (2019)
5.
Hass, H.* et al.: Benchmark problems for dynamic modeling of intracellular processes. Bioinformatics, accepted (2019)
6.
Ostaszewski, M.* et al.: Community-driven roadmap for integrated disease maps. Brief. Bioinform. 20, 659-670 (2019)
7.
Sinzger, M. ; Vanhoefer, J. ; Loos, C. & Hasenauer, J.: Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible. Sci. Rep. 9:3002 (2019)
8.
Villaverde, A.F.* ; Fröhlich, F. ; Weindl, D. ; Hasenauer, J. & Banga, J.R.*: Benchmarking optimization methods for parameter estimation in large kinetic models. Bioinformatics 35, 830-838 (2019)
9.
Apweiler, R.* et al.: Whither systems medicine? Exp. Mol. Med. 50:e453 (2018)
10.
Ballnus, B. ; Schaper, S.* ; Theis, F.J. & Hasenauer, J.: Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering. Bioinformatics 34, 494-501 (2018)
11.
Bast, L. et al.: Increasing neural stem cell division asymmetry and quiescence are predicted to contribute to the age-related decline in neurogenesis. Cell Rep. 25, 3231-3240.e8 (2018)
12.
Feigelman, J* ; Weindl, D. ; Theis, F.J. ; Marr, C. & Hasenauer, J.: LNA++: Linear noise approximation with first and second order sensitivities. In: Lecture Notes in Computer Science (16th International Conference on Computational Methods in Systems Biology, 12-14 September 2018, Brno; Czech Republic). 2018. 300-306 ( ; 11095 LNBI)
13.
Fröhlich, F. et al.: Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model. Cell Syst. 7, 567-579 (2018)
14.
Fröhlich, F. et al.: Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection. NPJ Syst. Biol. Appl. 5 (2018)
15.
Hross, S. ; Theis, F.J. ; Sixt, M.* & Hasenauer, J.: Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data. J. R. Soc. Interface 15:20180600 (2018)
16.
Isensee, J.* et al.: PKA-RII subunit phosphorylation precedes activation by cAMP and regulates activity termination. J. Cell Biol. 217, 2167-2184 (2018)
17.
Keller, S.* et al.: Effects of trastuzumab and afatinib on kinase activity in gastric cancer cell lines. Mol. Oncol. 12, 441-462 (2018)
18.
Klinger, E. ; Rickert, D. & Hasenauer, J.: pyABC: Distributed, likelihood-free inference. Bioinformatics 34, 3591-3593 (2018)
19.
Loos, C. ; Möller, K.* ; Fröhlich, F. ; Hucho, T.* & Hasenauer, J.: A hierarchical, data-driven approach to modeling single-cell populations predicts latent causes of cell-to-cell variability. Cell Syst. 6, 593-603.e13 (2018)
20.
Loos, C. ; Krause, S. & Hasenauer, J.: Hierarchical optimization for the efficient parametrization of ODE models. Bioinformatics 34, 4266-4273 (2018)