@misc{FackeldeyRoeblitzScharkoietal.2011, author = {Fackeldey, Konstantin and R{\"o}blitz, Susanna and Scharkoi, Olga and Weber, Marcus}, title = {Soft Versus Hard Metastable Conformations in Molecular Simulations}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-13189}, number = {11-27}, year = {2011}, abstract = {Particle methods have become indispensible in conformation dynamics to compute transition rates in protein folding, binding processes and molecular design, to mention a few. Conformation dynamics requires at a decomposition of a molecule's position space into metastable conformations. In this paper, we show how this decomposition can be obtained via the design of either ``soft'' or ``hard'' molecular conformations. We show, that the soft approach results in a larger metastabilitiy of the decomposition and is thus more advantegous. This is illustrated by a simulation of Alanine Dipeptide.}, language = {en} } @misc{Fackeldey2010, author = {Fackeldey, Konstantin}, title = {Coupling Meshbased and Meshfree Methods by a Transfer Operator Approach}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-11755}, number = {10-12}, year = {2010}, abstract = {In contrast to the well known meshbased methods like the finite element method, meshfree methods do not rely on a mesh. However besides their great applicability, meshfree methods are rather time consuming. Thus, it seems favorable to combine both methods, by using meshfree methods only in a small part of the domain, where a mesh is disadvantageous, and a meshbased method for the rest of the domain. We motivate, that this coupling between the two simulation techniques can be considered as saddle point problem and show the stability of this coupling. Thereby a novel transfer operator is introduced, which interacts in the transition zone, where both methods coexist.}, language = {en} } @misc{WeberFackeldey2013, author = {Weber, Marcus and Fackeldey, Konstantin}, title = {Computing the Minimal Rebinding Effect Included in a Given Kinetics}, issn = {1438-0064}, doi = {10.1137/13091124X}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-17796}, year = {2013}, abstract = {The rebinding effect is a phenomenon which occurs when observing a ligand-receptor binding process. On the macro scale this process comprises the Markov property. This Makovian view is spoiled when switching to the atomistic scale of a binding process. We therefore suggest a model which accurately describes the rebinding effect on the atomistic scale by allowing ''intermediate'' bound states. This allows us to define an indicator for the magnitude of rebinding and to formulate an optimization problem. The results form our examples show good agreement with data form laboratory.}, language = {en} } @misc{LieFackeldeyWeber2013, author = {Lie, Han Cheng and Fackeldey, Konstantin and Weber, Marcus}, title = {A square root approximation of transition rates for a Markov State Model}, issn = {1438-0064}, doi = {10.1137/120899959}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42195}, year = {2013}, abstract = {Trajectory- or mesh-based methods for analyzing the dynamical behavior of large molecules tend to be impractical due to the curse of dimensionality - their computational cost increases exponentially with the size of the molecule. We propose a method to break the curse by a novel square root approximation of transition rates, Monte Carlo quadrature and a discretization approach based on solving linear programs. With randomly sampled points on the molecular energy landscape and randomly generated discretizations of the molecular configuration space as our initial data, we construct a matrix describing the transition rates between adjacent discretization regions. This transition rate matrix yields a Markov State Model of the molecular dynamics. We use Perron cluster analysis and coarse-graining techniques in order to identify metastable sets in configuration space and approximate the transition rates between the metastable sets. Application of our method to a simple energy landscape on a two-dimensional configuration space provides proof of concept and an example for which we compare the performance of different discretizations. We show that the computational cost of our method grows only polynomially with the size of the molecule. However, finding discretizations of higher-dimensional configuration spaces in which metastable sets can be identified remains a challenge.}, language = {en} } @misc{NielsenFackeldeyWeber2013, author = {Nielsen, Adam and Fackeldey, Konstantin and Weber, Marcus}, title = {On a Generalized Transfer Operator}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-43162}, year = {2013}, abstract = {We introduce a generalized operator for arbitrary stochastic processes by using a pre-kernel, which is a generalization of the Markov kernel. For deterministic processes, such an operator is already known as the Frobenius-Perron operator, which is defined for a large class of measures. For Markov processes, there exists transfer operators being only well defined for stationary measures in \$L^2\$. Our novel generalized transfer operator is well defined for arbitrary stochastic processes, in particular also for deterministic ones. We can show that this operator is acting on \$L^1\$. For stationary measures, this operator is also an endomorphism of \$L^2\$ and, therefore, allows for a mathematical analysis in Hilbert spaces.}, language = {en} } @article{FackeldeyKoltaiNeviretal.2019, author = {Fackeldey, Konstantin and Koltai, Peter and Nevir, Peter and Rust, Henning and Schild, Axel and Weber, Marcus}, title = {From metastable to coherent sets - Time-discretization schemes}, volume = {29}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, doi = {10.1063/1.5058128}, pages = {012101 -- 012101}, year = {2019}, abstract = {In this article, we show that these well-established spectral algorithms (like PCCA+, Perron Cluster Cluster Analysis) also identify coherent sets of non-autonomous dynamical systems. For the identification of coherent sets, one has to compute a discretization (a matrix T) of the transfer operator of the process using a space-time-discretization scheme. The article gives an overview about different time-discretization schemes and shows their applicability in two different fields of application.}, language = {en} } @article{ReuterFackeldeyWeber2019, author = {Reuter, Bernhard and Fackeldey, Konstantin and Weber, Marcus}, title = {Generalized Markov modeling of nonreversible molecular kinetics}, volume = {17}, journal = {The Journal of Chemical Physics}, number = {150}, doi = {10.1063/1.5064530}, pages = {174103}, year = {2019}, abstract = {Markov state models are to date the gold standard for modeling molecular kinetics since they enable the identification and analysis of metastable states and related kinetics in a very instructive manner. The state-of-the-art Markov state modeling methods and tools are very well developed for the modeling of reversible processes in closed equilibrium systems. On the contrary, they are largely not well suited to deal with nonreversible or even nonautonomous processes of nonequilibrium systems. Thus, we generalized the common Robust Perron Cluster Cluster Analysis (PCCA+) method to enable straightforward modeling of nonequilibrium systems as well. The resulting Generalized PCCA (G-PCCA) method readily handles equilibrium as well as nonequilibrium data by utilizing real Schur vectors instead of eigenvectors. This is implemented in the G-PCCA algorithm that enables the semiautomatic coarse graining of molecular kinetics. G-PCCA is not limited to the detection of metastable states but also enables the identification and modeling of cyclic processes. This is demonstrated by three typical examples of nonreversible systems.}, language = {en} } @article{ErnstFackeldeyVolkameretal.2019, author = {Ernst, Natalia and Fackeldey, Konstantin and Volkamer, Andrea and Opatz, Oliver and Weber, Marcus}, title = {Computation of temperature-dependent dissociation rates of metastable protein-ligand complexes}, volume = {45}, journal = {Molecular Simulation}, number = {11}, doi = {10.1080/08927022.2019.1610949}, pages = {904 -- 911}, year = {2019}, abstract = {Molecular simulations are often used to analyse the stability of protein-ligand complexes. The stability can be characterised by exit rates or using the exit time approach, i.e. by computing the expected holding time of the complex before its dissociation. However determining exit rates by straightforward molecular dynamics methods can be challenging for stochastic processes in which the exit event occurs very rarely. Finding a low variance procedure for collecting rare event statistics is still an open problem. In this work we discuss a novel method for computing exit rates which uses results of Robust Perron Cluster Analysis (PCCA+). This clustering method gives the possibility to define a fuzzy set by a membership function, which provides additional information of the kind 'the process is being about to leave the set'. Thus, the derived approach is not based on the exit event occurrence and, therefore, is also applicable in case of rare events. The novel method can be used to analyse the temperature effect of protein-ligand systems through the differences in exit rates, and, thus, open up new drug design strategies and therapeutic applications.}, language = {en} } @misc{FackeldeySikorskiWeber2018, author = {Fackeldey, Konstantin and Sikorski, Alexander and Weber, Marcus}, title = {Spectral Clustering for Non-reversible Markov Chains}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-70218}, year = {2018}, abstract = {Spectral clustering methods are based on solving eigenvalue problems for the identification of clusters, e.g. the identification of metastable subsets of a Markov chain. Usually, real-valued eigenvectors are mandatory for this type of algorithms. The Perron Cluster Analysis (PCCA+) is a well-known spectral clustering method of Markov chains. It is applicable for reversible Markov chains, because reversibility implies a real-valued spectrum. We also extend this spectral clustering method to non-reversible Markov chains and give some illustrative examples. The main idea is to replace the eigenvalue problem by a real-valued Schur decomposition. By this extension non-reversible Markov chains can be analyzed. Furthermore, the chains do not need to have a positive stationary distribution. In addition to metastabilities, dominant cycles and sinks can also be identified. This novel method is called GenPCCA (i.e. Generalized PCCA), since it includes the case of non reversible processes. We also apply the method to real world eye tracking data.}, language = {en} } @article{FackeldeySikorskiWeber2018, author = {Fackeldey, Konstantin and Sikorski, Alexander and Weber, Marcus}, title = {Spectral Clustering for Non-Reversible Markov Chains}, volume = {37}, journal = {Computational and Applied Mathematics}, number = {5}, doi = {https://doi.org/10.1007/s40314-018-0697-0}, pages = {6376 -- 6391}, year = {2018}, abstract = {Spectral clustering methods are based on solving eigenvalue problems for the identification of clusters, e.g., the identification of metastable subsets of a Markov chain. Usually, real-valued eigenvectors are mandatory for this type of algorithms. The Perron Cluster Analysis (PCCA+) is a well-known spectral clustering method of Markov chains. It is applicable for reversible Markov chains, because reversibility implies a real-valued spectrum. We also extend this spectral clustering method to non-reversible Markov chains and give some illustrative examples. The main idea is to replace the eigenvalue problem by a real-valued Schur decomposition. By this extension non-reversible Markov chains can be analyzed. Furthermore, the chains do not need to have a positive stationary distribution. In addition to metastabilities, dominant cycles and sinks can also be identified. This novel method is called GenPCCA (i.e., generalized PCCA), since it includes the case of non-reversible processes. We also apply the method to real-world eye-tracking data.}, language = {en} } @article{LieFackeldeyWeber2013, author = {Lie, Han Cheng and Fackeldey, Konstantin and Weber, Marcus}, title = {A Square Root Approximation of Transition Rates for a Markov State Model}, volume = {34}, journal = {SIAM. J. Matrix Anal. Appl.}, number = {2}, doi = {10.1137/120899959}, pages = {738 -- 756}, year = {2013}, language = {en} } @article{ScharkoiFackeldeyMerkulowetal.2013, author = {Scharkoi, Olga and Fackeldey, Konstantin and Merkulow, Igor and Andrae, Karsten and Weber, Marcus and Nehls, Irene}, title = {Conformational Analysis of Alternariol on the Quantum Level}, volume = {19}, journal = {J. Mol. Model.}, number = {6}, doi = {10.1007/s00894-013-1803-2}, pages = {2567 -- 2572}, year = {2013}, language = {en} } @misc{BujotzekSchuettNielsenetal.2013, author = {Bujotzek, Alexander and Sch{\"u}tt, Ole and Nielsen, Adam and Fackeldey, Konstantin and Weber, Marcus}, title = {Efficient Conformational Analysis by Partition-of-Unity Coupling}, journal = {Math Chem}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42570}, year = {2013}, abstract = {Obtaining a sufficient sampling of conformational space is a common problem in molecular simulation. We present the implementation of an umbrella-like adaptive sampling approach based on function-based meshless discretization of conformational space that is compatible with state of the art molecular dynamics code and that integrates an eigenvector-based clustering approach for conformational analysis and the computation of inter-conformational transition rates. The approach is applied to three example systems, namely n-pentane, alanine dipeptide, and a small synthetic host-guest system, the latter two including explicitly modeled solvent.}, language = {en} } @misc{WeberFackeldeySchuette2017, author = {Weber, Marcus and Fackeldey, Konstantin and Sch{\"u}tte, Christof}, title = {Set-free Markov State Building}, issn = {1438-0064}, doi = {10.1063/1.4978501}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-62167}, year = {2017}, abstract = {Molecular dynamics (MD) simulations face challenging problems since the timescales of interest often are much longer than what is possible to simulate and even if sufficiently long simulation are possible the complex nature of the resulting simulation data makes interpretation difficult. Markov State Models (MSMs) help to overcome these problems by making experimentally relevant timescales accessible via coarse grained representations that also allows for convenient interpretation. However, standard set-based MSMs exhibit some caveats limiting their approximation quality and statistical significance. One of the main caveats results from the fact that typical MD trajectories repeatedly re-cross the boundary between the sets used to build the MSM which causes statistical bias in estimating the transition probabilities between these sets. In this article, we present a set-free approach to MSM building utilizing smooth overlapping ansatz functions instead of sets and an adaptive refinement approach. This kind of meshless discretization helps to overcome the recrossing problem and yields an adaptive refinement procedure that allows to improve the quality of the model while exploring state space and inserting new ansatz functions into the MSM.}, language = {en} } @incollection{DurmazFackeldeyWeber2011, author = {Durmaz, Vedat and Fackeldey, Konstantin and Weber, Marcus}, title = {A rapidly Mixing Monte Carlo Method for the Simulation of Slow Molecular Processes}, booktitle = {Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science}, editor = {Mode, Ch.}, publisher = {InTech}, year = {2011}, language = {en} } @inproceedings{FackeldeyKrauseKrause2008, author = {Fackeldey, Konstantin and Krause, Dorian and Krause, Rolf}, title = {Quadrature and Implementation of the Weak Coupling Method}, booktitle = {MMM 2008: Proceedings of the 4th International Conference on Multiscale Materials Modelling, 27-31 October 2008, Tallahassee, Fla.}, pages = {62 -- 65}, year = {2008}, language = {en} } @inproceedings{FackeldeyKrauseKrause2009, author = {Fackeldey, Konstantin and Krause, Dorian and Krause, Rolf}, title = {Weak Coupling Algorithms in Multiscale Simulations}, booktitle = {Proceedings of the third Inter. Confer. on Comp. Meth. for Coupled Problems in Science and Engrg}, pages = {1023pp}, year = {2009}, language = {en} } @inproceedings{FackeldeyKrauseKrause2010, author = {Fackeldey, Konstantin and Krause, Dorian and Krause, Rolf}, title = {Numerical Validation of Constraints Based Multiscale Methods}, booktitle = {Lecture Notes in Computational Science and Engineering}, editor = {Griebel, Michael and Schweitzer, M.}, pages = {141 -- 154}, year = {2010}, language = {en} } @article{FackeldeyKrauseKrauseetal.2011, author = {Fackeldey, Konstantin and Krause, Dorian and Krause, Rolf and Lenzen, Christoph}, title = {Coupling Molecular Dynamics and Continua with Weak Constraints}, volume = {9}, journal = {SIAM Multiscale Model. Simul.}, doi = {/10.1137/100782097}, pages = {1459 -- 1494}, year = {2011}, language = {en} } @inproceedings{FackeldeyKrauseSchweitzer2008, author = {Fackeldey, Konstantin and Krause, Rolf and Schweitzer, M.}, title = {Stability of Energy Transfer in the Weak Coupling Method}, volume = {65}, booktitle = {Meshfree Methods for Partial Differential Equations IV}, editor = {Griebel, Michael and Schweitzer, M.}, pages = {111 -- 121}, year = {2008}, language = {en} }