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  <doc>
    <id>8573</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>277</pageFirst>
    <pageLast>298</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>82</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-07-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Targeted multiobjective Dijkstra Algorithm</title>
    <abstract language="eng">We introduce the Targeted Multiobjective Dijkstra Algorithm (T-MDA), a label setting algorithm for the One-to-One Multiobjective Shortest Path (MOSP) Problem. It is based on the recently published Multiobjective Dijkstra Algorithm (MDA) and equips it with A*-like techniques. For any explored subpath, a label setting MOSP algorithm decides whether the subpath can be discarded or must be stored as part of the output. A major design choice is how to store subpaths from the moment they are first explored until the mentioned final decision can be made. The T-MDA combines the polynomially bounded size of the priority queue used in the MDA and alazy management of paths that are not in the queue. The running time bounds from the MDA remain valid. In practice, the T-MDA outperforms known algorithms from the literature and the increased memory consumption is negligible. In this paper, we benchmark the T-MDA against an improved version of the state of the art NAMOA∗drOne-to-One MOSP algorithm from the literature on a standard testbed.</abstract>
    <parentTitle language="eng">Networks</parentTitle>
    <identifier type="arxiv">2110.10978</identifier>
    <identifier type="doi">10.1002/net.22174</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Pedro Maristany de las Casas</author>
    <submitter>Pedro Maristany de las Casas</submitter>
    <author>Luitgard Kraus</author>
    <author>Antonio Sedeno-Noda</author>
    <author>Ralf Borndörfer</author>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="projects" number="LHS-ATN">LHS-ATN</collection>
    <collection role="persons" number="maristany">Maristany de las Casas, Pedro</collection>
    <collection role="institutes" number="neo">Network Optimization</collection>
    <collection role="projects" number="MODAL-MobilityLab">MODAL-MobilityLab</collection>
  </doc>
  <doc>
    <id>8512</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>903</pageFirst>
    <pageLast>966</pageLast>
    <pageNumber>64</pageNumber>
    <edition/>
    <issue/>
    <volume>197</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-02-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Implications, Conflicts, and Reductions for Steiner Trees</title>
    <parentTitle language="eng">Mathematical Programming</parentTitle>
    <identifier type="doi">10.1007/s10107-021-01757-5</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021-12-02</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Daniel Rehfeldt</author>
    <submitter> Rehfeldt</submitter>
    <author>Thorsten Koch</author>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="rehfeldt">Rehfeldt, Daniel</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="projects" number="MODAL-EnergyLab">MODAL-EnergyLab</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>8436</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>449</pageFirst>
    <pageLast>488</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>21</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimal Reaction Coordinates: Variational Characterization and Sparse Computation</title>
    <abstract language="eng">Reaction coordinates (RCs) are indicators of hidden, low-dimensional mechanisms that govern the long-term behavior of high-dimensional stochastic processes. We present a novel and general variational characterization of optimal RCs and provide conditions for their existence. Optimal RCs are minimizers of a certain loss function, and reduced models based on them guarantee a good approximation of the statistical long-term properties of the original high-dimensional process. We show that for slow-fast systems, metastable systems, and other systems with known good RCs, the novel theory reproduces previous insight. Remarkably, for reversible systems, the numerical effort required to evaluate the loss function scales only with the variability of the underlying, low-dimensional mechanism, and not with that of the full system. The theory provided lays the foundation for an efficient and data-sparse computation of RCs via modern machine learning techniques.</abstract>
    <parentTitle language="eng">Multiscale Modelling &amp; Simulation</parentTitle>
    <identifier type="arxiv">2107.10158</identifier>
    <identifier type="doi">10.1137/21M1448367</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="AcceptedDate">30.10.2022</enrichment>
    <author>Andreas Bittracher</author>
    <submitter>Ekaterina Engel</submitter>
    <author>Mattes Mollenhauer</author>
    <author>Péter Koltai</author>
    <author>Christof Schütte</author>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
  </doc>
  <doc>
    <id>8331</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>830</pageFirst>
    <pageLast>856</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>198</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Error Bounds for Discrete-Continuous Free Flight Trajectory Optimization</title>
    <abstract language="eng">Flight planning, the computation of optimal routes in view of flight time and  fuel consumption under given weather conditions, is traditionally done by finding globally shortest paths in a predefined airway network. Free flight trajectories, not restricted to a network, have the potential to reduce the costs significantly, and can be computed using locally convergent continuous optimal control methods.&#13;
&#13;
Hybrid methods that start with a discrete global search and refine with a fast continuous local optimization combine the best properties of both approaches, but rely on a good switchover, which requires error estimates for discrete paths relative to continuous trajectories.&#13;
    &#13;
Based on vertex density and local complete connectivity, we derive localized and a priori bounds for the flight time of discrete paths relative to the optimal continuous trajectory, and illustrate their properties on a set of benchmark problems. It turns out that localization improves the error bound by four orders of magnitude, but still leaves ample opportunities for tighter bounds using a posteriori error estimators.</abstract>
    <parentTitle language="eng">Journal of Optimization Theory and Applications</parentTitle>
    <identifier type="doi">10.1007/s10957-023-02264-7</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="AcceptedDate">2023-06-19</enrichment>
    <author>Ralf Borndörfer</author>
    <submitter>Martin Weiser</submitter>
    <author>Fabian Danecker</author>
    <author>Martin Weiser</author>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="weiser">Weiser, Martin</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="institutes" number="neo">Network Optimization</collection>
    <collection role="projects" number="MathPlus-AA3-3">MathPlus-AA3-3</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/8331/article.pdf</file>
  </doc>
  <doc>
    <id>8274</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>607</issue>
    <volume>13</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-01-12</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Modelling altered signalling of G-protein coupled receptors in inflamed environment to advance drug design</title>
    <abstract language="eng">We previously reported the successful design, synthesis and testing of the prototype opioid painkiller NFEPP that does not elicit adverse side effects. The design process of NFEPP was based on mathematical modelling of extracellular interactions between G-protein coupled receptors (GPCRs) and ligands, recognizing that GPCRs function differently under pathological versus healthy conditions. We now present an additional and novel stochastic model of GPCR function that includes intracellular dissociation of G-protein subunits and modulation of plasma membrane calcium channels and their dependence on parameters of inflamed and healthy tissue (pH, radicals). The model is validated against in vitro experimental data for the ligands NFEPP and fentanyl at different pH values and radical concentrations. We observe markedly reduced binding affinity and calcium channel inhibition for NFEPP at normal pH compared to lower pH, in contrast to the effect of fentanyl. For increasing radical concentrations, we find enhanced constitutive G-protein activation but reduced ligand binding affinity. Assessing the different effects, the results suggest that, compared to radicals, low pH is a more important determinant of overall GPCR function in an inflamed environment. Future drug design efforts should take this into account.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="doi">10.1038/s41598-023-27699-w</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="AcceptedDate">2023-01-06</enrichment>
    <author>Arne Thies</author>
    <submitter>Erlinda Körnig</submitter>
    <author>Vikram Sunkara</author>
    <author>Sourav Ray</author>
    <author>Hanna Wulkow</author>
    <author>M. Özgür Celik</author>
    <author>Fatih Yergöz</author>
    <author>Christof Schütte</author>
    <author>Christoph Stein</author>
    <author>Marcus Weber</author>
    <author>Stefanie Winkelmann</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmol">Computational Molecular Design</collection>
    <collection role="institutes" number="compsys">Computational Systems Biology</collection>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="persons" number="weber">Weber, Marcus</collection>
    <collection role="projects" number="SFB1114-C3">SFB1114-C3</collection>
    <collection role="persons" number="winkelmann">Winkelmann, Stefanie</collection>
    <collection role="persons" number="sunkara">Sunkara, Vikram</collection>
    <collection role="projects" number="MathPlusAA1-1">MathPlusAA1-1</collection>
    <collection role="institutes" number="MfLMS">Mathematics for Life and Materials Science</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="projects" number="MathPlusAA1-5">MathPlusAA1-5</collection>
    <collection role="projects" number="MathPlusAA1-15">MathPlusAA1-15</collection>
  </doc>
  <doc>
    <id>8216</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>A579</pageFirst>
    <pageLast>A595</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>45</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-04-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning</title>
    <parentTitle language="eng">SIAM Journal on Scientific Computing</parentTitle>
    <identifier type="doi">10.1137/21M1412682</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Michael Dellnitz</author>
    <submitter>Marvin Lücke</submitter>
    <author>Eyke Hüllermeier</author>
    <author>Marvin Lücke</author>
    <author>Sina Ober-Blöbaum</author>
    <author>Christian Offen</author>
    <author>Sebastian Peitz</author>
    <author>Karlson Pfannschmidt</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compsys">Computational Systems Biology</collection>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="persons" number="luecke">Lücke, Marvin</collection>
  </doc>
  <doc>
    <id>8031</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>151</pageFirst>
    <pageLast>161</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>20</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-12-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data</title>
    <abstract language="eng">Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks.</abstract>
    <parentTitle language="eng">IEEE/ACM Transactions on Computational Biology and Bioinformatics</parentTitle>
    <identifier type="doi">10.1109/tcbb.2022.3141656</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021-12-22</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Sahar Iravani</author>
    <submitter>Kalina Tsankova</submitter>
    <author>Tim Conrad</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="conrad">Conrad, Tim</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
