@article{VillatoroZuehlkeRiebeetal.2016, author = {Villatoro, Jos{\´e} and Z{\"u}hlke, Martin and Riebe, Daniel and Beitz, Toralf and Weber, Marcus and Riedel, Jens and L{\"o}hmannsr{\"o}ben, Hans-Gerd}, title = {IR-MALDI ion mobility spectrometry: physical source characterization and application as HPLC detector}, journal = {International Journal for Ion Mobility Spectrometry}, number = {19/4}, doi = {10.1007/s12127-016-0208-1}, pages = {197 -- 297}, year = {2016}, language = {en} } @misc{Nielsen2016, author = {Nielsen, Adam}, title = {The Monte Carlo Computation Error of Transition Probabilities}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-59933}, year = {2016}, abstract = {In many applications one is interested to compute transition probabilities of a Markov chain. This can be achieved by using Monte Carlo methods with local or global sampling points. In this article, we analyze the error by the difference in the \$L^2\$ norm between the true transition probabilities and the approximation achieved through a Monte Carlo method. We give a formula for the error for Markov chains with locally computed sampling points. Further, in the case of reversible Markov chains, we will deduce a formula for the error when sampling points are computed globally. We will see that in both cases the error itself can be approximated with Monte Carlo methods. As a consequence of the result, we will derive surprising properties of reversible Markov chains.}, language = {en} } @article{Nielsen2016, author = {Nielsen, Adam}, title = {The Monte Carlo Computation Error of Transition Probabilities}, volume = {118}, journal = {Statistics \& Probability Letters}, publisher = {Elsevier}, doi = {10.1016/j.spl.2016.06.011}, pages = {163 -- 170}, year = {2016}, abstract = {In many applications one is interested to compute transition probabilities of a Markov chain. This can be achieved by using Monte Carlo methods with local or global sampling points. In this article, we analyze the error by the difference in the \$L^2\$ norm between the true transition probabilities and the approximation achieved through a Monte Carlo method. We give a formula for the error for Markov chains with locally computed sampling points. Further, in the case of reversible Markov chains, we will deduce a formula for the error when sampling points are computed globally. We will see that in both cases the error itself can be approximated with Monte Carlo methods. As a consequence of the result, we will derive surprising properties of reversible Markov chains.}, language = {en} } @phdthesis{Durmaz2016, author = {Durmaz, Vedat}, title = {Atomistic Binding Free Energy Estimations for Biological Host-Guest Systems}, publisher = {FU Dissertationen Online}, pages = {216}, year = {2016}, abstract = {Accurate quantifications of protein-ligand binding affinities by means of in silico methods increasingly gain importance in various scientific branches including toxicology and pharmacology. In silico techniques not only are generally less demanding than laboratory experiments regarding time as well as cost, in particular, if binding assays or synthesis protocols need to be developed in advance. At times, they also provide the only access to risk assessments on novel chemical compounds arising from biotic or abiotic degradation of anthropogenic substances. However, despite the continuous technological and algorithmic progress over the past decades, binding free energy estimations through molecular dynamics simulations still pose an enormous computational challenge owed to the mathematical complexity of solvated macromolecular systems often consisting of hundreds of thousands of atoms. The goals of this thesis can roughly be divided into two categories dealing with different aspects of host-guest binding quantification. On the one side algorithmic strategies for a comprehensive exploration and decomposition of conformational space in conjunction with an automated selection of representative molecular geometries and binding poses have been elaborated providing initial structures for free energy calculations. In light of the dreaded trapping problem typically associated with molecular dynamics simulations, the focus was laid on a particularly systematic generation of representatives covering a broad range of physically accessible molecular conformations and interaction modes. On the other side and ensuing from these input geometries, binding affinity models based on the linear interaction energy (LIE) method have been developed for a couple of (bio)molecular systems. The applications included a successful prediction of the liquid-chromatographic elution order as well as retention times of highly similar hexabromocyclododecane (HBCD) stereoisomers, a novel empirical LIE-QSAR hybrid binding affinity model related to the human estrogen receptor α (ERα), and, finally, the (eco)toxicological prioritization of transformation products originating from the antibiotic sulfamethoxazole with respect to their binding affinities to the bacterial enzyme dihydropteroate synthase. Altogether, a fully automated approach to binding mode and affinity estimation has been presented that is content with an arbitrary geometry of a small molecule under observation and a spatial vector specifying the binding site of a potential target molecule. According to our studies, it is superior to conventional docking and thermodynamic average methods and primarily suggesting binding free energy calculation on the basis of several heavily distinct complex geometries. Both chromatographic retention times of HBCD and binding affinities to ERα yielded squared coefficients of correlation with experimental results significantly higher than 0.8. Approximately 85 \% (100 \%) of predicted receptor-ligand binding modes deviated less than 1.53 {\AA} (2.05 {\AA}) from available crystallographic structures.}, language = {en} } @article{HeyeBeckerLuetkeEverslohetal.2016, author = {Heye, Katharina and Becker, Dennis and L{\"u}tke-Eversloh, Christian and Durmaz, Vedat and Ternes, Thomas and Oetken, Matthias and Oehlmann, J{\"o}rg}, title = {Effects of carbamazepine and two of its metabolites on the non-biting midge Chironomus riparius in a sediment full life cycle toxicity test}, volume = {98}, journal = {Water Research}, pages = {19 -- 27}, year = {2016}, abstract = {The antiepileptic drug carbamazepine (CBZ) and its main metabolites carbamazepine-10,11-epoxide (EP-CBZ) and 10,11-dihydro-10,11-dihydroxy-carbamazepine (DiOH-CBZ) were chosen as test substances to assess chronic toxicity on the non-biting midge Chironomus riparius. All three substances were tested in a 40-day sediment full life cycle test (according to OECD 233) in which mortality, emergence, fertility, and clutch size were evaluated. In addition, these parameters were integrated into the population growth rate to reveal population relevant effects. With an LC50 of 0.203 mg/kg (time-weighted mean), the metabolite EP-CBZ was significantly more toxic than the parent substance CBZ (LC50: 1.11 mg/kg). Especially mortality, emergence, and fertility showed to be sensitive parameters under the exposure to CBZ and EP-CBZ. By using classical molecular dynamics (MD) simulations, the binding of CBZ to the ecdysone receptor was investigated as one possible mode of action but showed to be unlikely. The second metabolite DiOH-CBZ did not show any effects within the tested concentration rage (0.171 - 1.22 mg/kg). Even though CBZ was less toxic compared to EP-CBZ, CBZ is found in the environment at much higher concentrations and causes therefore a higher potential risk for sediment dwelling organisms compared to its metabolites. Nevertheless, the current study illustrates the importance of including commonly found metabolites into the risk assessment of parent substances.}, language = {en} } @article{DjurdjevacConradWeberSchuette2016, author = {Djurdjevac Conrad, Natasa and Weber, Marcus and Sch{\"u}tte, Christof}, title = {Finding dominant structures of nonreversible Markov processes}, volume = {14}, journal = {Multiscale Modeling and Simulation}, number = {4}, doi = {10.1137/15M1032272}, pages = {1319 -- 1340}, year = {2016}, language = {en} } @article{KrebekvonAchaziSollederetal.2016, author = {Krebek, von, Larissa K. S. and Achazi, Andreas J. and Solleder, Marthe and Weber, Marcus and Paulus, Beate and Schalley, Christoph A.}, title = {Allosteric and Chelate Cooperativity in Divalent Crown Ether-Ammonium Complexes with Strong Binding Enhancements}, volume = {22}, journal = {Chem. Eur. J.}, number = {43}, doi = {10.1002/chem.201603098}, pages = {15475 -- 15484}, year = {2016}, language = {en} } @misc{Grever2016, type = {Master Thesis}, author = {Grever, Andreas}, title = {Estimating Missing Entries of a Partial Mean First Passage Time Matrix}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65228}, year = {2016}, language = {en} } @misc{Solleder2016, type = {Master Thesis}, author = {Solleder, Marthe}, title = {Molecular Dynamics Simulations : What is the Effect of a Spin Probe on the Drug Loading of a Nanocarrier}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65239}, year = {2016}, language = {en} } @article{VillatoroZuehlkeRiebeetal.2016, author = {Villatoro, Jose and Z{\"u}hlke, Martin and Riebe, Daniel and Riedel, Jens and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd}, title = {IR-MALDI ion mobility spectrometry}, volume = {408}, journal = {Analytical and Bioanalytical Chemistry}, number = {23}, doi = {10.1007/s00216-016-9739-x}, pages = {6259 -- 6268}, year = {2016}, language = {en} } @misc{WitzigBeckenbachEifleretal.2016, author = {Witzig, Jakob and Beckenbach, Isabel and Eifler, Leon and Fackeldey, Konstantin and Gleixner, Ambros and Grever, Andreas and Weber, Marcus}, title = {Mixed-Integer Programming for Cycle Detection in Non-reversible Markov Processes}, issn = {1438-0064}, doi = {10.1137/16M1091162}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-60353}, year = {2016}, abstract = {In this paper, we present a new, optimization-based method to exhibit cyclic behavior in non-reversible stochastic processes. While our method is general, it is strongly motivated by discrete simulations of ordinary differential equations representing non-reversible biological processes, in particular molecular simulations. Here, the discrete time steps of the simulation are often very small compared to the time scale of interest, i.e., of the whole process. In this setting, the detection of a global cyclic behavior of the process becomes difficult because transitions between individual states may appear almost reversible on the small time scale of the simulation. We address this difficulty using a mixed-integer programming model that allows us to compute a cycle of clusters with maximum net flow, i.e., large forward and small backward probability. For a synthetic genetic regulatory network consisting of a ring-oscillator with three genes, we show that this approach can detect the most productive overall cycle, outperforming classical spectral analysis methods. Our method applies to general non-equilibrium steady state systems such as catalytic reactions, for which the objective value computes the effectiveness of the catalyst.}, language = {en} }