Browse by Projects
Number of items: 42. 2023Straube, Arthur V. and Winkelmann, Stefanie and Höfling, Felix (2023) Accurate reduced models for the pH oscillations in the urea-urease reaction confined to giant lipid vesicles. J. Phys. Chem. B, 127 (13). pp. 2955-2967. del Razo, M.J. and Winkelmann, S. and Klein, R. and Höfling, F. (2023) Chemical diffusion master equation: formulations of reaction–diffusion processes on the molecular level. J. Math. Phys., 64 (013304). Thies, Arne and Sunkara, Vikram and Ray, Sourav and Wulkow, Hanna and Özgür Celik, M. and Yergöz, Fatih and Schütte, Christof and Stein, Christoph and Weber, Marcus and Winkelmann, Stefanie (2023) Modelling altered signalling of G-protein coupled receptors in inflamed environment to advance drug design. Scientific Reports, 13 (1). pp. 1-12. 2022del Razo, Mauricio J. and Frömberg, Daniela and Straube, Arthur V. and Schütte, Christof and Höfling, Felix and Winkelmann, Stefanie (2022) A probabilistic framework for particle-based reaction–diffusion dynamics using classical Fock space representations. Letters in Mathematical Physics, 112 (49). pp. 1-59. Boltz, Horst-Holger and Sirbu, Alexei and Stelzer, Nina and de Lanerolle, Primal and Winkelmann, Stefanie and Annibale, Paolo (2022) The impact of membrane protein diffusion on GPCR signaling. Cells, 11 (10). Montefusco, Alberto and Schütte, Christof and Winkelmann, Stefanie (2022) A route to the hydrodynamic limit of a reaction-diffusion master equation using gradient structures. arXiv . (Submitted) Ernst, Ariane and Schütte, Christof and Sigrist, Stephan J. and Winkelmann, Stefanie (2022) Variance of filtered signals: Characterization for linear reaction networks and application to neurotransmission dynamics. Mathematical Biosciences, 343 . Schütte, Christof and Klus, Stefan and Hartmann, Carsten (2022) Overcoming the Timescale Barrier in Molecular Dynamics: Transfer Operators, Variational Principles, and Machine Learning. ZIB . (Submitted) 2021Kostré, Margarita and Schütte, Christof and Noé, Frank and del Razo, Mauricio J. (2021) Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs. Sociaty for Industrial and Applied Mathematics, 19 (4). Straube, Arthur V. and Winkelmann, Stefanie and Schütte, Christof and Höfling, Felix (2021) Stochastic pH Oscillations in a Model of the Urea−Urease Reaction Confined to Lipid Vesicles. J. Phys. Chem. Lett., 12 . pp. 1-6. del Razo, Mauricio J. and Dibak, Manuel and Schütte, Christof and Noé, Frank (2021) Multiscale molecular kinetics by coupling Markov state models and reaction-diffusion dynamics. The Journal of Chemical Physics, 155 (12). Frömberg, Daniela and Höfling, Felix (2021) Generalized master equation for first-passage problems in partitioned spaces. Journal of Physics A, 54 (21). pp. 1-29. Winkelmann, Stefanie and Zonker, J. and Schütte, Christof and Djurdjevac Conrad, Natasa (2021) Mathematical modeling of spatio-temporal population dynamics and application to epidemic spreading. Mathematical Biosciences, 336 . Niemann, Jan-Hendrik and Winkelmann, Stefanie and Wolf, S. and Schütte, Christof (2021) Agent-based modeling: Population limits and large timescales. Choas, 31 (3). Helfmann, L. and Djurdjevac Conrad, N. and Djurdjevac, A. and Winkelmann, S. and Schütte, Ch. (2021) From interacting agents to density-based modeling with stochastic PDEs. Commun. Appl. Math. Comput. Sci., 16 (1). pp. 1-32. ISSN Online: 2157-5452; Print: 1559-3940 Tierno, Pietro and Johansen, Tom H. and Straube, Arthur V. (2021) Thermally active nanoparticle clusters enslaved by engineered domain wall traps. NATURE COMMUNICATIONS, 12 (5813). pp. 1-11. 2020Husic, Brooke E. and Charron, Nicholas E. and Lemm, Dominik and Wang, Jiang and Pérez, Adrià and Majewski, Maciej and Krämer, Andreas and Chen, Yaoyi and Olsson, Simon and de Fabritiis, Gianni and Noé, Frank and Clementi, Cecilia (2020) Coarse graining molecular dynamics with graph neural networks. J. Chem. Phys., 153 (194101). pp. 1-17. Sadeghi, Mohsen and Noé, Frank (2020) Large-scale simulation of biomembranes incorporating realistic kinetics into coarse-grained models. Nature Communications, 11 (2951). Winkelmann, Stefanie and Schütte, Christof (2020) Stochastic Dynamics in Computational Biology. Buchreihe: Frontiers in Applied Dynamical Systems: Reviews and Tutorials . 2019Dibak, Manuel and Fröhner, Christoph and Noé, Frank and Höfling, Felix (2019) Diffusion-influenced reaction rates in the presence of pair interactions. The Journal of Chemical Physics, 151 (16). Wang, J. and Olsson, S. and Wehmeyer, C. and Perez, A. and Charron, N.E. and de Fabritiis, G. and Noé, F. and Clementi, C. (2019) Machine Learning of coarse-grained Molecular Dynamics Force Fields. ACS Cent. Sci., 5 (5). pp. 755-767. ISSN 2374-7943, ESSN: 2374-7951 Paul, F. and Wu, H. and Vossel, M. and de Groot, B.L. and Noé, F. (2019) Identification of kinetic order parameters for non-equilibrium dynamics. J. Chem. Phys., 150 (16). p. 164120. ISSN 0021-9606, ESSN: 1089-7690 Pinamonti, G. and Paul, F. and Noé, F. and Rodriguez, A. and Bussi, G. (2019) The mechanism of RNA base fraying: Molecular dynamics simulations analyzed with core-set Markov state models. J. Chem. Phys., 150 (15). p. 154123. ISSN 0021-9606, ESSN: 1089-7690 Hoffmann, M. and Fröhner, Chr. and Noé, F. (2019) ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics. PLoS Computational Biology, 15 (2). e1006830. ISSN 1553-7358 Hoffmann, M. and Fröhner, Chr. and Noé, F. (2019) Reactive SINDy: Discovering governing reactions from concentration data. J. Chem. Phys., 150 (2). 025101. ISSN 0021-9606, ESSN: 1089-7690 2018Scherer, M. K. and Husic, B.E. and Hoffmann, M. and Paul, F. and Wu, H. and Noé, F. (2018) Variational Selection of Features for Molecular Kinetics. SFB 1114 Preprint in arXiv:1811.11714 . pp. 1-12. (Unpublished) Wehmeyer, C. and Scherer, M. K. and Hempel, T. and Husic, B.E. and Olsson, S. and Noé, F. (2018) Introduction to Markov state modeling with the PyEMMA software — v1.0. LiveCoMS, 1 (1). pp. 1-12. ISSN E-ISSN: 2575-6524 Fröhner, Chr. and Noé, F. (2018) Reversible interacting-particle reaction dynamics. J. Phys. Chem. B, 122 (49). pp. 11240-11250. del Razo, M.J. and Qian, H. and Noé, F. (2018) Grand canonical diffusion-influenced reactions: a stochastic theory with applications to multiscale reaction-diffusion simulations. J. Chem. Phys., 149 (4). 044102. ISSN 0021-9606, ESSN: 1089-7690 Dibak, M. and del Razo, M.J. and De Sancho, D. and Schütte, Ch. and Noé, F. (2018) MSM/RD: Coupling Markov state models of molecular kinetics with reaction-diffusion simulations. Journal of Chemical Physics, 148 (214107). ISSN 0021-9606 Sadeghi, M. and Weikl, T. and Noé, F. (2018) Particle-based membrane model for mesoscopic simulation of cellular dynamics. J. Chem. Phys., 148 (4). 044901. 2017Sbailò, L. and Noé, F. (2017) An efficient multi-scale Green's Functions Reaction Dynamics scheme. J. Chem. Phys., 147 . p. 184106. ISSN 0021-9606, ESSN: 1089-7690 Paul, F. and Wehmeyer, C. and Abualrous, E. T. and Wu, H. and Crabtree, M. D. and Schöneberg, J. and Clarke, J. and Freund, C. and Weikl, T. and Noé, F. (2017) Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations. Nat. Comm., 8 (1095). Winkelmann, S. and Schütte, Ch. (2017) Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems. Journal of Chemical Physics, 147 (11). pp. 1-18. Olsson, Simon and Wu, H. and Paul, F. and Clementi, C. and Noé, F. (2017) Combining experimental and simulation data of molecular processes via augmented Markov models. Proc. Natl. Acad. Sci. USA, 114 . pp. 8265-8270. Pinamonti, G. and Zhao, J. and Condon, D. and Paul, F. and Noé, F. and Turner, D. and Bussi, G. (2017) Predicting the kinetics of RNA oligonucleotides using Markov state models. J. Chem. Theory Comput., 13 (2). pp. 926-934. Schöneberg, J. and Lehmann, M. and Ullrich, A. and Posor, Y. and Lo, W.-T. and Lichtner, G. and Schmoranzer, J. and Haucke, V. and Noé, F. (2017) Lipid-mediated PX-BAR domain recruitment couples local membrane constriction to endocytic vesicle fission. Nat. Comm., 8 . p. 15873. 2016Winkelmann, S. and Schütte, Ch. (2016) The spatiotemporal master equation: approximation of reaction-diffusion dynamics via Markov state modeling. Journal of Chemical Physics, 145 (21). p. 214107. Albrecht, D. and Winterflood, C. M. and Sadeghi, M. and Tschager, T. and Noé, F. and Ewers, H. (2016) Nanoscopic compartmentalization of membrane protein motion at the axon initial segment. J. Cell Biol., 215 (1). pp. 37-46. Wu, H. and Paul, F. and Wehmeyer, C. and Noé, F. (2016) Multiensemble Markov models of molecular thermodynamics and kinetics. Proceedings of the National Academy of Sciences, 113 (23). E3221-E3230 . ISSN 0027-8424 2015Trendelkamp-Schroer, B. and Wu, H. and Paul, F. and Noé, F. (2015) Estimation and uncertainty of reversible Markov models. J. Chem. Phys., 143 (17). p. 174101. Scherer, M. K. and Trendelkamp-Schroer, B. and Paul, F. and Pérez-Hernández, G. and Hoffmann, M. and Plattner, N. and Wehmeyer, C. and Prinz, J.-H. and Noé, F. (2015) PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models. J. Chem. Theory Comput., 11 (11). pp. 5525-5542. |