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41.
Geissen, E.M.* et al.: MEMO - multi-experiment mixture model analysis of censored data. Bioinformatics 32, 2464-2472 (2016)
42.
Hross, S. & Hasenauer, J.: Analysis of CFSE time-series data using division-, age- and label-structured population models. Bioinformatics 32, 2321-2329 (2016)
43.
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)
44.
Kazeroonian, A. ; Fröhlich, F. ; Raue, A.* ; Theis, F.J. & Hasenauer, J.: CERENA: ChEmical REaction Network Analyzer - a toolbox for the simulation and analysis of stochastic chemical kinetics. PLoS ONE 11:e0146732 (2016)
45.
Loos, C. ; Fiedler, A. & Hasenauer, J.: Parameter estimation for reaction rate equation constrained mixture models.  Lecture Notes Comp. Sci. 9859, 186-200 (2016)
46.
Luber, B.* et al.: Identification of predictive response and resistance factors to targeted therapy in gastric cancer using a systems medicine approach. Eur. J. Cancer 68, S135-S135 (2016)
47.
Filipczyk, A. et al.: Network plasticity of pluripotency transcription factors in embryonic stem cells. Nat. Cell Biol. 17, 1235-1246 (2015)
48.
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)
49.
Hug, S. ; Schwarzfischer, M. ; Hasenauer, J. ; Marr, C. & Theis, F.J.: An adaptive scheduling scheme for calculating Bayes factors with thermodynamic integration using Simpson’s rule. Stat. Comp. 26, 663-677 (2015)
50.
Loos, C. ; Marr, C. ; Theis, F.J. & Hasenauer, J.: Approximate bayesian computation for stochastic single-cell time-lapse data using multivariate test statistics. Lecture Notes Comp. Sci. 9308, 52-63 (2015)
51.
Marr, C. ; Hasenauer, J. & Theis, F.J.: Models and methods for systems biology and systems medicine: The Institute of Computational Biology at the Helmholtz Zentrum München. Systembiologie.de 9, 40-43 (2015)
52.
Raue, A.* et al.: Data2Dynamics: A modeling environment tailored to parameter estimation in dynamical systems. Bioinformatics 31, 3558-3560 (2015)
53.
Fröhlich, F. ; Theis, F.J. & Hasenauer, J.: Uncertainty analysis for non-identifiable dynamical systems: Profile likelihoods, bootstrapping and more. Lecture Notes Comp. Sci. 8859, 61-72 (2014)
54.
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)
55.
Hasenauer, J. ; Wolf, V.* ; Kazeroonian, A. & Theis, F.J.: Method of conditional moments (MCM) for the chemical master equation: A unified framework for the method of moments and hybrid stochastic-deterministic models. J. Math. Biol. 69, 687-735 (2014)
56.
Hasenauer, J. ; Hasenauer, C.* ; Hucho, T.* & Theis, F.J.: ODE constrained mixture modelling: A method for unraveling subpopulation structures and dynamics. PLoS Comput. Biol. 10:e1003686 (2014)
57.
Isensee, J.* et al.: Pain modulators regulate the dynamics of PKA-RII phosphorylation in subgroups of sensory neurons. J. Cell Sci. 127, 216-229 (2014)
58.
Kazeroonian, A. ; Theis, F.J. & Hasenauer, J.: Modeling of stochastic biological processes with non-polynomial propensities using non-central conditional moment equation. IFAC PapersOnline 19, 1729-1735 (2014)
59.
Löhning, M.* ; Reble, M.* ; Hasenauer, J. ; Yu, S.* & Allgöwer, F.*: Model predictive control using reduced order models: Guaranteed stability for constrained linear systems. J. Process Contr. 24, 1647-1659 (2014)
60.
Neumann, S.* ; Hasenauer, J. ; Pollak, N.* & Scheurich, P.*: Dominant negative effects of TNF-Related Apoptosis-Inducing Ligand (TRAIL) receptor 4 on TRAIL receptor 1 signaling by formation of heteromeric complexes. J. Biol. Chem. 289, 16576-16587 (2014)