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1.
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)
2.
Stapor, P. et al.: PESTO: Parameter EStimation TOolbox. Bioinformatics 34, 705-707 (2018)
3.
Boiger, R.* ; Hasenauer, J. ; Hross, S. & Kaltenbacher, B.*: Integration based profile likelihood calculation for PDE constrained parameter estimation problems. Inverse Probl. 32:125009 (2016)
4.
Hross, S. & Hasenauer, J.: Analysis of CFSE time-series data using division-, age- and label-structured population models. Bioinformatics 32, 2321-2329 (2016)
5.
Hross, S. ; Fiedler, A. ; Theis, F.J. & Hasenauer, J.: Quantitative comparison of competing PDE models for Pomlp dynamics in fission yeast. In: (Foundations of Systems Biology in Engineering - FOSBE 2016, 9—12 October 2016, Magdeburg). 2016. 264-269 (IFAC PapersOnline ; 49-26)
6.
Hross, S.: Parameter estimation and uncertainty quantification for image based systems biology. München, Technische Universität, Fakultät für Mathematik, Diss., 2016, 130 S.
7.
Hasenauer, J. ; Jagiella, N. ; Hross, S. & Theis, F.J.: Data-driven modelling of biological multi-scale processes. J. Coupled Syst. Multiscale Dyn. 3, 101-121 (2015)
8.
Fröhlich, F. ; Hross, S. ; Theis, F.J. & Hasenauer, J.: Radial basis function approximations of Bayesian parameter posterior densities for uncertainty analysis. Lecture Notes Comp. Sci. 8859, 73-85 (2014)