Items where Subject is "Physical Sciences"
Group by: Creators | Item Type Number of items at this level: 77. AAbramyan, A. and Stolzenberg, S. and Li, Z. and Loland, C. J. and Noé, F. and Shi, L. (2017) The isomeric preference of an atypical dopamine transporter inhibitor contributes to its selection of the transporter conformation. ACS Chem. Neurosc., 8 . pp. 1735-1746. BBiedermann, J. and Ullrich, A. and Schöneberg, J. and Noé, F. (2015) ReaDDyMM: fast interacting-particle reaction-diffusion simulations using graphical processing units. Biophys. J., 108 . pp. 457-461. ISSN 00063495 Boninsegna, L. and Gobbo, G. and Noé, F. and Clementi, C. (2015) Investigating Molecular Kinetics by Variationally Optimized Diffusion Maps. J. Chem. Theory Comput., 11 . pp. 5947-5960. Bowman, G. R. and Pande, V. S. and Noé, F. (2014) Introduction and overview of this book. In: An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicine and Biology, 797 . Springer, pp. 1-6. CChodera, J. D. and Noé, F. (2014) Markov state models of biomolecular conformational dynamics. Curr. Opin. Struct. Biol., 25 . pp. 135-144. Chodera, J. D. and Noé, F. (2010) Probability distributions of molecular observables computed from Markov models. II: Uncertainties in observables and their time-evolution. J. Chem. Phys, 133 (10). p. 105102. Chodera, J. D. and Swope, W. D. and Noé, F. and Prinz, J.-H. and Shirts, M. R. and Pande, V. S. (2011) Dynamical reweighting: Improved estimates of dynamical properties from simulations at multiple temperatures. J. Chem. Phys., 134 (24). p. 244107. DDaldrop, J.O. and Kowalik, B.G. and Netz, R.R. (2017) External Potential Modifies Friction of Molecular Solutes in Water. Phys. Rev. X, 7 (4). 041065. Donadio, D. and Ghiringhelli, L. M. and Delle Site, L. (2012) Autocatalytic and cooperatively-stabilized dissociation of water on a stepped platinum surface. Journal of the American Chemical Society, 134 . pp. 19217-19222. FFaelber, Katja and Posor, York and Held, M. and Roske, Yvette and Schulze, Dennis and Haucke, Volker and Noé, F. and Daumke, Oliver (2011) Crystal structure of nucleotide-free dynamin. Nature, 477 . pp. 556-560. Fröhner, Christoph (2015) Reversible integrators for particle based reaction-diffusion simulations. Masters thesis, FU Berlin. GGerber, S. and Horenko, I. (2017) Toward a direct and scalable identification of reduced models for categorical processes. Proceedings of the National Academy of Sciences, 114 (19). pp. 4863-4868. HHeld, M. and Imhof, P. and Keller, B. and Noé, F. (2012) Modulation of a ligand’s energy landscape and kinetics by the chemical environment. J. Phys. Chem. B, 116 . pp. 13597-13607. Held, M. and Metzner, Ph. and Prinz, J.-H. and Noé, F. (2011) Mechanisms of Protein-Ligand association and its modulation by protein mutations. Biophys. J., 100 (3). pp. 701-710. IImhof, P. and Noé, F. (2006) AM1/d Parameters for Magnesium in Metalloenzymes. J. Chem. Theo. Comput., 2 . pp. 1050-1056. KKappler, J. and Netz, R.R. (2015) Multiple surface wave solutions on linear viscoelastic media. EPL, 112 (1). p. 19002. Kappler, J. and Netz, R.R. (2017) Pulse propagation at interfaces and their possible relevance for biology. Journal Club for Condensed Matter Physics . Kappler, J. and Shrivastava, S. and Schneider, M.F. and Netz, R.R. (2017) Nonlinear fractional waves at elastic interfaces. Phys. Rev. Fluids, 2 (11). p. 114804. Kavalar, Martin (2010) Conformational Dynamics of a Peptide Ligand in Solvent and in Complex with a MHC-I Protein. Masters thesis, FU Berlin. Keller, B. and Hünenberger, Philippe and van Gunsteren, Wilfred (2011) An Analysis of the Validity of Markov State Models for Emulating the Dynamics of Classical Molecular Systems and Ensembles. J. Chem. Theo. Comput., 7 . pp. 1032-1044. Keller, B. and Kobitski, A. and Jäschke, A. and Nienhaus, G.U. and Noé, F. (2014) Complex RNA folding kinetics revealed by single molecule FRET and hidden Markov models. J. Am. Chem. Soc., 136 . pp. 4534-4543. Keller, B. and Prinz, J.-H. and Noé, F. (2012) Markov models and dynamical fingerprints: unraveling the complexity of molecular kinetics. Chem. Phys., 396 . pp. 92-107. Keller, B. and Prinz, J.-H. and Noé, F. (2012) Resolving the apparent gap in complexity between simulated and measured kinetics of biomolecules. From Computational Biophysics to Systems Biology (CBSB11) Proceedings, IAS Series, 8 . pp. 61-64. Kim, W.K. and Netz, R.R. (2015) The mean shape of transition and first-passage paths. J. Chem. Phys., 143 (224108). Koltai, P. and Wu, H. and Noé, F. and Schütte, Ch. (2018) Optimal data-driven estimation of generalized Markov state models for non-equilibrium dynamics. Computation, 6(1) (22). ISSN 2079-3197 (online) LLindner, Benjamin and Yi, Zheng and Prinz, J.-H. and Smith, J. C. and Noé, F. (2013) Dynamic Neutron Scattering from Conformational Dynamics I: Theory and Markov models. J. Chem. Phys., 139 . p. 175101. Lux, A. and Schäfer, M. and Bergmann, M. and Jahn, T. and Marg, O. and Nagy, E. and Ransiek, A. and Theiler, L. (2019) Societal effects of transdisciplinary sustainability research – How can they be strengthened during the research process? Environmental Science & Policy, 101 . pp. 183-191. Lüdge, Torsten (2009) A theoretical model of conformational transitions in biomolecules based on single-molecule Förster resonance electron transfer measurements. Masters thesis, FU Berlin and TU Berlin. MMardt, A. and Pasquali, L. and Wu, H. and Noé, F. (2018) VAMPnets: Deep learning of molecular kinetics. Nat. Comm., 9 . p. 5. Mey, A.S.J.S. and Wu, H. and Noé, F. (2014) xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states. Phys. Rev. X, 4 (4). 041018. NNoé, F. and Chodera, J. D. (2014) Uncertainty Estimation. In: An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicine and Biology, 797 . Springer, pp. 61-74. Noé, F. and Prinz, J.-H. (2014) Analysis of Markov Models. In: An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicine and Biology, 797 . Springer, pp. 75-90. Noé, F. (2015) Beating the Millisecond Barrier in Molecular Dynamics Simulations. Biophys J., 108 . pp. 228-229. Noé, F. and Banisch, Ralf and Clementi, C. (2016) Commute maps: separating slowly-mixing molecular configurations for kinetic modeling. J. Chem. Theory Comput., 12 . pp. 5620-5630. Noé, F. and Clementi, C. (2017) Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods. Curr. Opin. Struct. Biol., 43 . pp. 141-147. Noé, F. and Clementi, C. (2015) Kinetic distance and kinetic maps from molecular dynamics simulation. J. Chem. Theory Comput., 11 . pp. 5002-5011. Noé, F. and Daidone, I. and Smith, J. C. and Di Nola, A. and Amadei, A. (2008) Solvent Electrostriction Driven Peptide Folding revealed by Quasi-Gaussian Entropy Theory and Molecular Dynamics Simulation. J. Phys. Chem. B, 112 . pp. 11155-11163. Noé, F. and Doose, S. and Daidone, I. and Löllmann, M. and Chodera, J. D. and Sauer, M. and Smith, J. C. (2011) Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. Proc. Natl. Acad. Sci. USA, 108 . pp. 4822-4827. Noé, F. and Wu, H. and Prinz, J.-H. and Plattner, N. (2013) Projected and Hidden Markov Models for calculating kinetics and metastable states of complex molecules. J. Chem. Phys., 139 . p. 184114. Nüske, F. and Keller, B. and Pérez-Hernández, G. and Mey, A.S.J.S. and Noé, F. (2014) Variational Approach to Molecular Kinetics. J. Chem. Theory Comput., 10 . pp. 1739-1752. Nüske, F. and Schneider, R. and Vitalini, F. and Noé, F. (2016) Variational Tensor Approach for Approximating the Rare-Event Kinetics of Macromolecular Systems. J. Chem. Phys., 144 (5). 054105. OOlsson, 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. PPaul, 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). Peuker, Sebastian and Cukkemane, Abhishek and Held, M. and Noé, F. and Kaupp, Benjamin and Seifert, Reinhard (2013) Kinetics of ligand-receptor interaction reveals an induced-fit mode of binding in a cyclic nucleotide-activated protein. Biophys. J., 104 . pp. 63-74. Plattner, N. and Doerr, S. and De Fabritiis, G. and Noé, F. (2017) Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat. Chem., 9 . pp. 1005-1011. Plattner, N. and Noé, F. (2015) Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models. Nat. Commun., 6 . p. 7653. Prinz, J.-H. and Chodera, J. D. and Noé, F. (2014) Estimation and Validation of Markov Models. In: An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicine and Biology , 797 . Springer, pp. 45-60. Prinz, J.-H. (2012) Advanced estimation methods for Markov models of dynamical systems. PhD thesis, FU Berlin. Prinz, J.-H. and Chodera, J. D. and Noé, F. (2014) Spectral rate theory for two-state kinetics. Phys Rev X, 4 . 011020. Prinz, J.-H. and Chodera, J. D. and Pande, V. S. and Swope, W. D. and Smith, J. C. and Noé, F. (2011) Optimal use of data in parallel tempering simulations for the construction of discrete-state Markov models of biomolecular dynamics. J. Chem. Phys., 134 (24). p. 244108. Prinz, J.-H. and Keller, B. and Noé, F. (2011) Probing molecular kinetics with Markov models: Metastable states, transition pathways and spectroscopic observables. Phys. Chem. Chem. Phys., 13 . pp. 16912-16927. Prinz, J.-H. and Wu, H. and Sarich, M. and Keller, B. and Senne, M. and Held, M. and Chodera, J. D. and Schütte, Ch. and Noé, F. (2011) Markov models of molecular kinetics: Generation and Validation. J. Chem. Phys., 134 . p. 174105. Pérez-Hernández, G. and Noé, F. (2016) Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems. J. Chem. Theory Comput., 12 . pp. 6118-6129. Pérez-Hernández, G. and Paul, F. and Giorgino, T. and de Fabritiis, G. and Noé, F. (2013) Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys., 139 . 015102. RRansiek, A. and Wundrak, R. (2016) Dialogue and Narration. International Journal of Cross-Cultural Studies and Environmental Communication, 5 (1). pp. 41-51. SSadeghi, M. and Weikl, T. and Noé, F. (2018) Particle-based membrane model for mesoscopic simulation of cellular dynamics. J. Chem. Phys., 148 (4). 044901. Sbailò, 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 Schaller, Christoph (2013) STORMicroscopy: A Mathematical Analysis. Masters thesis, FU Berlin. 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. Schlaich, A. and Kappler, J. and Netz, R.R. (2017) Hydration Friction in Nanoconfinement: From Bulk via Interfacial to Dry Friction. Nano Lett., 17 (10). pp. 5969-5976. Schmidt, B. and Lorenz, U. (2014) WavePacket 5.0: A Matlab program package for quantum-mechanical wavepacket propagation and time-dependent spectroscopy. Freely available at SourceForge.net. Schulz, R. and Hansen, Y. von and Daldrop, J.O. and Kappler, J. and Noé, F. and Netz, R.R. (2018) Markov state modeling reveals competing collective hydrogen bond rearrangements in liquid water. SFB 1114 Preprint 02/2018 . (Unpublished) Schulz, R. and Yamamoto, K. and Klossek, A. and Flesch, R. and Hönzke, S. and Rancan, F. and Vogt, A. and Blume-Peytavi, U. and Hedtrich, S. and Schäfer-Korting, M. and Rühl, E. and Netz, R.R. (2017) Data-based modeling of drug penetration relates human skin barrier function to the interplay of diffusivity and free-energy profiles. PNAS, 114 (14). pp. 3631-3636. ISSN 1091-6490 (online) Schöneberg, J. and Heck, M. and Hofmann, K. P. and Noé, F. (2014) Explicit Spatio-temporal Simulation of Receptor-G Protein Coupling in Rod Cell Disk Membranes. Biophys. J., 107 . pp. 1042-1053. ISSN 00063495 Steger, Katrin and Bollmann, Stefan and Noé, F. and Doose, S. (2013) Systematic evaluation of fluorescence correlation spectroscopy data analysis on the nanosecond time scale. Phys. Chem. Chem. Phys., 15 . pp. 10435-10445. TTrendelkamp-Schroer, B. and Noé, F. (2016) Efficient estimation of rare-event kinetics. Phys. Rev. X, 6 . 011009. Trendelkamp-Schroer, B. and Noé, F. (2013) Efficient Bayesian estimation of Markov model transition matrices with given stationary distribution. J. Phys. Chem., 138 . p. 164113. Trendelkamp-Schroer, B. and Wu, H. and Noé, F. (2016) Reversible Markov chain estimation using convex-concave programming. arXiv . 1603.01640. Trendelkamp-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. VVitalini, F. and Mey, A.S.J.S. and Noé, F. and Keller, B. (2015) Dynamic Properties of Force Fields. J. Chem. Phys., 142 . 084101. WWu, H. and Mey, A.S.J.S. and Rosta, E. and Noé, F. (2014) Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states. J. Chem. Phys., 141 (21). p. 214106. Wu, H. and Noé, F. (2015) Gaussian Markov transition models of molecular kinetics. J. Chem. Phys., 142 (8). 084104. Wu, H. and Noé, F. (2014) Optimal estimation of free energies and stationary densities from multiple biased simulations. SIAM Multiscale Model. Simul., 12 . pp. 25-54. Wu, H. and Noé, F. (2016) Spectral learning of dynamic systems from nonequilibrium data. NIPS, 29 . pp. 4179-4187. Wu, H. and Nüske, F. and Paul, F. and Klus, S. and Koltai, Péter and Noé, F. (2017) Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations. J. Chem. Phys., 146 . p. 154104. Wu, Ho and Noé, Frank (2019) Variational approach for learning Markov processes from time series data. Journal of Nonlinear Science, 30 . pp. 23-66. ISSN 1432-1467 (online) YYi, Zheng and Lindner, Benjamin and Prinz, J.-H. and Noé, F. and Smith, J. C. (2013) Dynamic Neutron Scattering from Conformational Dynamics II: Application using Molecular Dynamics Simulation and Markov modeling. J. Chem. Phys., 139 . p. 175102. |