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Mardt, Andreas and Pasquali, Luca and Noé, F. and Wu, Hao (2020) Deep learning Markov and Koopman models with physical constraints. Proceedings of Machine Learning Research, 107 . pp. 451-475.

Noé, F. and Olsson, S. and Köhler, J. and Wu, H. (2019) Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning. Science, 365 (6457). eaaw1147.

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

Schulz, R. and von Hansen, Y. and Daldrop, J.O. and Kappler, J. and Noé, F. and Netz, R.R. (2018) Collective hydrogen-bond rearrangement dynamics in liquid water. J. Chem. Phys., 149 (24). -244504. ISSN 0021-9606, ESSN: 1089-7690

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

Scherer, 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 (Unpublished)

Swenson, D.W.H. and Prinz, J.-H. and Noé, F. and Chodera, J. D. and Bolhuis, P.G. (2018) OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics. Journal of Chemical Theory and Computation, Article ASAP . ISSN 1549-9618, ESSN: 15-49-9626

Swenson, D.W.H. and Prinz, J.-H. and Noé, F. and Chodera, J. D. and Bolhuis, P.G. (2018) OpenPathSampling: A Python Framework for Path Sampling Simulations. 2. Building and Customizing Path Ensembles and Sample Schemes. Journal of Chemical Theory and Computation, Article ASAP . ISSN 1549-9618, ESSN: 15-49-9626

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

Wehmeyer, C. and Noé, F. (2018) Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics. The Journal of Chemical Physics, 148 (24). p. 241703. ISSN 0021-9606

Kappler, J. and Noé, F. and Netz, R.R. (2018) Cyclization dynamics of finite-length collapsed self-avoiding polymers. SFB 1114 Preprint 02/2018 . (Unpublished)

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)

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)

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.

Gerber, S. and Olsson, S. and Noé, F. and Horenko, I. (2018) A scalable approach to the computation of invariant measures for high-dimensional Markovian systems. Scientific Reports, 8 (1796). ISSN 2045-2322

Klus, S. and Nüske, F. and Koltai, P. and Wu, H. and Kevrekidis, I. and Schütte, Ch. and Noé, F. (2018) Data-driven model reduction and transfer operator approximation. Journal of Nonlinear Science, 28 (1). pp. 1-26.

Paul, F. and Noé, F. and Weikl, T. (2018) Identifying Conformational-Selection and Induced-Fit Aspects in the Binding-Induced Folding of PMI from Markov State Modeling of Atomistic Simulations. J. Phys. Chem. B .

Mardt, A. and Pasquali, L. and Wu, H. and Noé, F. (2018) VAMPnets: Deep learning of molecular kinetics. Nat. Comm., 9 . p. 5.

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

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).

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.

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.

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.

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.

Nüske, F. and Wu, H. and Wehmeyer, C. and Clementi, C. and Noé, F. (2017) Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias. J. Chem. Phys., 146 . 094104.

Olsson, Simon and Noé, F. (2017) Mechanistic models of chemical exchange induced relaxation in protein NMR. J. Am. Chem. Soc., 139 . pp. 200-210.

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.

Wieczorek, M. and Abualrous, E. T. and Sticht, J. and Alvaro-Benito, M. and Stolzenberg, S. and Noé, F. and Freund, C. (2017) Title: Major Histocompatibility Complex (MHC) class I and MHC class II proteins: Conformational Plasticity in Antigen Presentation. Frontiers Immunology, 8 . p. 292.

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.

Abramyan, 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.

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

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.

Trendelkamp-Schroer, B. and Noé, F. (2016) Efficient estimation of rare-event kinetics. Phys. Rev. X, 6 . 011009.

Doerr, S. and Harvey, M. J. and Noé, F. and De Fabritiis, G. (2016) HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. J. Chem. Theory Comput., 12 . pp. 1845-1852.

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.

Wieczorek, M. and Sticht, J. and Stolzenberg, S. and Günther, S. and Wehmeyer, C. and El Habre, Z. and Àlvaro-Benito, M. and Noé, F. and Freund, C. (2016) MHC class II complexes sample intermediate states along the peptide exchange pathway. Nature Communications, 7 . p. 13224.

Vitalini, F. and Noé, F. and Keller, B. (2016) Molecular dynamics simulations data of the twenty encoded amino acids in different force fields. Data in Brief, 7 . pp. 582-590.

Trendelkamp-Schroer, B. and Wu, H. and Noé, F. (2016) Reversible Markov chain estimation using convex-concave programming. arXiv . 1603.01640.

Wu, H. and Noé, F. (2016) Spectral learning of dynamic systems from nonequilibrium data. NIPS, 29 . pp. 4179-4187.

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.

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.

Noé, F. (2015) Beating the Millisecond Barrier in Molecular Dynamics Simulations. Biophys J., 108 . pp. 228-229.

Reubold, T. F. and Faelber, K. and Plattner, N. and Posor, Y. and Branz, K. and Curth, U. and Schlegel, J. and Anand, R. and Manstein, D. and Noé, F. and Haucke, V. and Daumke, O. and Eschenburg, S. (2015) Crystal structure of the dynamin tetramer. Nature, 525 . pp. 404-408.

Vitalini, F. and Mey, A.S.J.S. and Noé, F. and Keller, B. (2015) Dynamic Properties of Force Fields. J. Chem. Phys., 142 . 084101.

Ullrich, A. and Böhme, M. A. and Schöneberg, J. and Depner, H. and Sigrist, S. J. and Noé, F. (2015) Dynamical organization of Syntaxin-1A at the presynaptic active zone. PLoS Comput. Biol., 11 . e1004407.

Wu, H. and Noé, F. (2015) Gaussian Markov transition models of molecular kinetics. J. Chem. Phys., 142 (8). 084104.

Gunkel, M. and Schöneberg, J. and Alkhaldi, W. and Irsen, S. and Noé, F. and Kaupp, U. B. and Al-Amoudi, A. (2015) Higher-order architecture of rhodopsin in intact photoreceptors and its implication for phototransduction kinetics. Structure, 23 . pp. 628-638.

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.

Noé, F. and Clementi, C. (2015) Kinetic distance and kinetic maps from molecular dynamics simulation. J. Chem. Theory Comput., 11 . pp. 5002-5011.

Wu, H. and Prinz, J.-H. and Noé, F. (2015) Projected Metastable Markov Processes and Their Estimation with Observable Operator Models. J. Chem. Phys., 143 (14). p. 144101.

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.

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.

Biedermann, 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

Schror, M. and Mey, A.S.J.S. and Noé, F. and MacPhee, C. (2015) Shedding Light on the Dock-Lock Mechanism in Amyloid Fibril Growth Using Markov State Models. J. Chem. Phys. Lett., 6 . pp. 1076-1081.

Noé, F. (2015) Statistical inefficiency of Markov model count matrices. preprint . (Unpublished)

Vitalini, F. and Noé, F. and Keller, B. (2015) A basis set for peptides for the variational approach to conformational kinetics. J. Chem. Theory Comput., 11 . pp. 3992-4004.

Wu, 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.

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.

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.

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

Chodera, J. D. and Noé, F. (2014) Markov state models of biomolecular conformational dynamics. Curr. Opin. Struct. Biol., 25 . pp. 135-144.

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.

Schöneberg, J. and Ullrich, Alexander and Noé, F. (2014) Simulation tools for particle-based reaction-diffusion dynamics in continuous space. BMC Biophysics, 7 . p. 11.

Prinz, J.-H. and Chodera, J. D. and Noé, F. (2014) Spectral rate theory for two-state kinetics. Phys Rev X, 4 . 011020.

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.

Lindner, 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.

Yi, 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.

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.

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.

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.

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.

Schöneberg, J. and Noé, F. (2013) ReaDDy - a software for particle based reaction diffusion dynamics in crowded cellular environments. PLoS ONE, 8 . e74261. ISSN 1932-6203

Posor, Y. and Eichhorn-Gruenig, M. and Puchkov, D. and Schöneberg, J. and Ullrich, A. and Lampe, A. and Müller, R. and Zarbakhsh, S. and Gulluni, F. and Hirsch, E. and Krauss, M. and Schultz, C. and Schmoranzer, J. and Noé, F. and Haucke, V. (2013) Spatiotemporal control of endocytosis by phosphatidylinositol-3,4-bisphosphate. Nature, 499 . pp. 233-237.

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.

Noé, F. and Nüske, F. (2013) A variational approach to modeling slow processes in stochastic dynamical systems. SIAM Multiscale Model. Simul., 11 . pp. 635-655.

Held, M. and Noé, F. (2012) Calculating kinetics and pathways of protein–ligand association. Eur. J. Cell Biol., 91 . pp. 357-364.

Senne, M. and Trendelkamp-Schroer, B. and Mey, A.S.J.S. and Schütte, Ch. and Noé, F. (2012) EMMA - A software package for Markov model building and analysis. Journal of Chemical Theory and Computation, 8 . pp. 2223-2238.

Sadiq, S. K. and Noé, F. and de Fabritiis, G. (2012) Kinetic characterization of the critical step in HIV-1 protease maturation. Proc. Natl. Acad. Sci. USA, 109 . pp. 20449-20454.

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.

Held, 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.

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.

Faelber, K. and Held, M. and Gao, S. and Posor, Y. and Haucke, V. and Noé, F. and Daumke, O. (2012) Structural insights into dynamin-mediated membrane fission. Structure, 20 . 1621-1628.

Schütte, Ch. and Noé, F. and Lu, Jianfeng and Sarich, M. and Vanden-Eijnden, E. (2011) Markov State Models Based on Milestoning. J. Chem. Phys., 134 (20). p. 204105.

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.

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.

Wu, H. and Noé, F. (2011) Bayesian framework for modeling diffusion processes with nonlinear drift based on nonlinear and incomplete observations. Phys. Rev. E, 83 (3). 036705.

Faelber, 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.

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.

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.

Prinz, J.-H. and Held, M. and Smith, J. C. and Noé, F. (2011) Efficient Computation, Sensitivity and Error Analysis of Committor Probabilities for Complex Dynamical Processes. Multiscale Model. Simul., 9 . pp. 545-567.

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.

Splettstößer, T. and Holmes, K. C. and Noé, F. and Smith, J. C. (2011) Structural modeling and molecular dynamics simulation of the actin filament. Proteins, 79 . pp. 2033-2043.

Wu, H. and Noé, F. (2011) A flat Dirichlet process switching model for Bayesian estimation of hybrid systems. Procedia Computer Science, 4 . pp. 1393-1402.

Wu, H. and Noé, F. (2010) Maximum a posteriori estimation for Markov chains based on Gaussian Markov random fields. Procedia Computer Science, 1 (1). pp. 1665-1673.

Sarich, M. and Noé, F. and Schütte, Ch. (2010) On the Approximation Quality of Markov State Models. Multiscale Model. Simul., 8 (4). pp. 1154-1177.

Bernhard, S. and Noé, F. (2010) Optimal Identification of Semi-Rigid Domains in Macromolecules from Molecular Dynamics Simulation. PLoS One, 5 . e10491.

Wu, H. and Noé, F. (2010) Probability Distance Based Compression of Hidden Markov Models. Multiscale Model. Simul., 8 . pp. 1838-1861.

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.

Noé, F. and Schütte, Ch. and Vanden-Eijnden, E. and Reich, L. and Weikl, T. (2009) Constructing the Full Ensemble of Folding Pathways from Short Off-Equilibrium Simulations. Proc. Natl. Acad. Sci. USA, 106 (45). pp. 19011-19016.

Splettstößer, T. and Noé, F. and Oda, T. and Smith, J. C. (2009) Nucleotide-dependence of G-actin conformation from multiple molecular dynamics simulations and observation of a putatively polymerisation-competent superclosed state. Proteins, 76 . pp. 353-364.

Schütte, Ch. and Noé, F. and Meerbach, E. and Metzner, Ph. and Hartmann, C. (2009) Conformation Dynamics. Proceedings of the 6th International Congress on Industrial and Applied Mathematics, I. Jeltsch and G. Wanner (eds.), . pp. 297-335.

Metzner, Ph. and Noé, F. and Schütte, Ch. (2009) Estimating the Sampling Error: Distribution of Transition Matrices and Functions of Transition Matrices for Given Trajectory Data. Phys. Rev. E, 80 (2). 021106.

Noé, F. (2008) Probability Distributions of Molecular Observables computed from Markov Models. J. Chem. Phys., 128 . p. 244103.

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 Fischer, S. (2008) Transition Networks for Modeling the Kinetics of Conformational Change in Macromolecules. Curr. Opin. Struct. Biol., 18 . pp. 154-162.

Noé, F. and Horenko, I. and Schütte, Ch. and Smith, J. C. (2007) Hierarchical Analysis of Conformational Dynamics in Biomolecules: Transition Networks of Metastable States. J. Chem. Phys., 126 (15). p. 155102.

Horenko, I. and Hartmann, C. and Schütte, Ch. and Noé, F. (2007) Data-based Parameter Estimation of Generalized Multidimensional Langevin Processes. Phys. Rev. E, 76 (01). 016706.

Noé, F. and Oswald, M. and Reinelt, G. (2007) Optimization in Graphs with Limited Information on the Edge Weights. Operations Research Proceedings 2007. Editors: J. Kalcsics and S. Nickel . pp. 435-440.

Noé, F. and Smith, J. C. (2007) Transition Networks: a unifying theme for molecular simulation and computer science. Mathematical Modeling of Biological Systems I, A. Deutsch, L. Brusch, H. Byrne, G. de Vries and H.-P. Herzel (Eds) . pp. 125-144.

Noé, F. and Smith, J. C. and Schütte, Ch. (2007) A network-based approach to biomolecular dynamics. From Computational Biophysics to Systems Biology (CBSB07). Editors: U. H. E. Hansmann, J. Meinke, S. Mohanty and O. Zimmermann, NIC Se .

Imhof, P. and Noé, F. (2006) AM1/d Parameters for Magnesium in Metalloenzymes. J. Chem. Theo. Comput., 2 . pp. 1050-1056.

Noé, F. and Oswald, M. and Reinelt, G. and Smith, J. C. and Fischer, S. (2006) Computing Best Transition Pathways in High-Dimensional Dynamical Systems. Multisc. Model. Sim., 5 . pp. 393-419.

Becker, T. and Fischer, S. and Noé, F. and Tournier, A. and Ullmann, M. and Kurkal, V. and Smith, J. C. (2006) Physical and functional aspects of protein dynamics. Soft Condensed Matter Physics in Molecular and Cell Biology, Poon & Andelman (Eds) .

Noé, F. and Krachtus, D. and Smith, J. C. and Fischer, S. (2006) Transition Networks for the Comprehensive Characterization of Complex Conformational Change in Proteins. J. Chem. Theo. Comput., 2 . pp. 840-857.

Noé, F. and Ille, F. and Smith, J. C. and Fischer, S. (2005) Automated Computation of Low-Energy Pathways for Complex Rearrangements in Proteins: Application to the conformational switch of Ras p21. Proteins, 59 . pp. 534-544.

Noé, F. and Schwarzl, S. and Fischer, S. and Smith, J. C. (2003) Computational tools for analysing structural changes in proteins in solution. Applied Bioinformatics, 2 . pp. 11-17.

Becker, T. and Fischer, S. and Noé, F. and Ullmann, M. and Tournier, A. and Smith, J. C. (2003) Protein Dynamics: Glass Transition and Mechanical Function. Advances in Solid State Physics, 43 . pp. 677-694.

Book Section

Schütte, Ch. and Deuflhard, P. and Noé, F. and Weber, M. (2014) Design of functional molecules. In: MATHEON : mathematics for key technologies. EMS series in industrial and applied mathematics, 1 . EMS Publishing House, Zürich, pp. 49-65. ISBN 978-3-03719-137-8

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.

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.

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.

Sarich, M. and Prinz, J.-H. and Schütte, Ch. (2014) Markov Model Theory. In: An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicine and Biology, 797 (797). Springer, Dordrecht, Heidelberg, New York, London, pp. 23-44. ISBN 978-94-007-7605-0

Noé, 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. (2013) Markov Models of Molecular Kinetics. In: Encyclopedia of Biophysics. Springer, pp. 1385-1394.

Faelber, K. and Gao, S. and Held, M. and Posor, Y. and Haucke, V. and Noé, F. and Daumke, O. (2013) Oligomerization of dynamin superfamily proteins in health and disease. In: Progress in Molecular Biology and Translational Science. Elsevier, pp. 411-443.

Book

Bowman, G. R. and Pande, V. S. and Noé, F., eds. (2014) An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicine and Biology, 797 . Springer.

Thesis

Noé, F. (2006) Transition Networks: Computational Methods for the Comprehensive Analysis of Complex Rearrangements in Proteins. PhD thesis, University of Heidelberg.

Noé, F. (2002) The Evolution of Cell Colonies in Volvocacean Algae: Investigation by theoretical analysis and computer simulation. Masters thesis, Cork Institute of Technology, Ireland.