@inproceedings{WeberKube2005, author = {Weber, Marcus and Kube, Susanna}, title = {Robust Perron Cluster Analysis for Various Applications in Computational Life Science}, booktitle = {Computational Life Sciences}, pages = {57 -- 66}, year = {2005}, language = {en} } @inproceedings{KubeWeber2008, author = {Kube, Susanna and Weber, Marcus}, title = {Computation of equilibrium densities in metastable dynamical systems by domain decomposition}, volume = {1048}, booktitle = {Numerical Analysis and Applied Mathematics, International Conference on Numerical Analysis and Applied Mathematics 2008}, publisher = {AIP Conference Proceedings}, pages = {339 -- 342}, year = {2008}, language = {en} } @inproceedings{KubeWeber2008, author = {Kube, Susanna and Weber, Marcus}, title = {Preserving the Markov Property of Reduced Reversible Markov Chains}, volume = {1048}, booktitle = {Numerical Analysis and Applied Mathematics, International Conference on Numerical Analysis and Applied Mathematics 2008}, pages = {593 -- 596}, year = {2008}, language = {en} } @inproceedings{FackeldeyBujotzekWeber2012, author = {Fackeldey, Konstantin and Bujotzek, Alexander and Weber, Marcus}, title = {A meshless discretization method for Markov state models applied to explicit water peptide folding simulations}, volume = {89}, booktitle = {Meshfree Methods for Partial Differential Equations VI}, publisher = {Springer}, pages = {141 -- 154}, year = {2012}, language = {en} } @inproceedings{IgdeWoelkRoeblitzetal.2015, author = {Igde, Sinaida and W{\"o}lk, Hendrik and R{\"o}blitz, Susanna and Reidelbach, Marco and Weber, Marcus and Hartmann, Laura}, title = {Identifying Multivalent Binding Kinetics of Precision Glycomacromolecules: A Kinetic Study Using kinITC}, booktitle = {M{\"u}nster Symposium on Cooperative Effects 2015 - SFB 858, at Westf{\"a}lische Wilhelms-Universit{\"a}t M{\"u}nster, 2015}, year = {2015}, abstract = {Multivalent sugar/protein interactions are well-known to proceed through different binding modes 1-5 which in turn can be described by their binding kinetics 3-5. This study provides additional insight into the association and dissociation reaction rates of complex multivalent sugar/protein interactions. Binding kinetics of recently introduced multivalent precision glycomacromolecules 6-8 to Concanavalin A (Con A) were studied by " kinetic Isothermal Titration Calorimetry " (kinITC) 9-11. The effect of multivalency is evaluated by comparing rate constants of glycomacromolecules obtaining the same and different valency of mannose ligands and by variation of the overall backbone properties, such as hydrophilic/ hydrophoboc. In addition, binding kinetics were studied using different conformations of Con A (homodimer vs.-tetramer) and thus a different protein valency. Our results show that precision glycomacromolecule/Con A binding proceeds non-cooperatively. Further, association and dissociation rates are mainly described by intermolecular complex formation. Together with the so-called functional valency, we can discriminate between " bound " and " unbound " states for macroscopic on-and off-rates, even for such complex glycooligomer/protein systems. By comparing e.g. a mono-to a divalent glycomacromolecule for their binding to dimeric Con A, we see a lower dissociation rate for the latter. As both bind monovalently to Con A, this is a strong indication for a statistical rebinding event. Further, there is a strong dependence of multivalent binding kinetics on the ligand density of glycomacromolecules as well as the Con A conformation and thus the overall on-and off-rates.}, language = {en} } @inproceedings{ReidelbachSchemberaWeber2024, author = {Reidelbach, Marco and Schembera, Bj{\"o}rn and Weber, Marcus}, title = {Towards a FAIR Documentation of Workflows and Models in Applied Mathematics}, volume = {14749}, booktitle = {Mathematical Software - ICMS 2024}, edition = {Lecture Notes in Computer Science}, publisher = {Springer Nature Switzerland}, address = {Cham}, arxiv = {http://arxiv.org/abs/2403.17778}, doi = {10.1007/978-3-031-64529-7_27}, pages = {254 -- 262}, year = {2024}, abstract = {Modeling-Simulation-Optimization workflows play a fundamental role in applied mathematics. The Mathematical Research Data Initiative, MaRDI, responded to this by developing a FAIR and machine-interpretable template for a comprehensive documentation of such workflows. MaRDMO, a Plugin for the Research Data Management Organiser, enables scientists from diverse fields to document and publish their workflows on the MaRDI Portal seamlessly using the MaRDI template. Central to these workflows are mathematical models. MaRDI addresses them with the MathModDB ontology, offering a structured formal model description. Here, we showcase the interaction between MaRDMO and the MathModDB Knowledge Graph through an algebraic modeling workflow from the Digital Humanities. This demonstration underscores the versatility of both services beyond their original numerical domain.}, language = {en} } @inproceedings{YousefianDonatiSikorskietal.2026, author = {Yousefian, Maryam and Donati, Luca and Sikorski, Alexander and Weber, Marcus and R{\"o}blitz, Susanna}, title = {Exploring Metastable Dynamics of Gene Regulatory Networks with ISOKANN}, volume = {15959}, booktitle = {Computational Methods in Systems Biology. CMSB 2025}, doi = {10.1007/978-3-032-01436-8_8}, pages = {126 -- 149}, year = {2026}, abstract = {Stochastic dynamical systems like gene regulatory networks (GRNs) often exhibit behavior characterized by metastable sets (representing cellular phenotypes), in which trajectories remain for long times, whereas switches between these sets in the phase space are rare events. One way to capture these rare events is to infer the system's long-term behavior from the spectral characteristics (eigenvalues and eigenvectors) of its Koopman operator. For GRNs, the Koopman operator is based on the chemical master equation (CME), which provides a precise mathematical modeling framework for stochastic GRNs. Since the CME is typically analytically intractable, methods based on discretizing the CME operator have been developed. However, determining the number and location of metastable sets in the phase space as well as the transition rates between them remains computationally challenging, especially for large GRNs with many genes and interactions. A promising alternative method, called ISOKANN (invariant subspaces of Koopman operators with artificial neural networks) has been developed in the context of molecular dynamics. ISOKANN uses a combination of the power iteration and neural networks to learn the basis functions of an invariant subspace of the Koopman operator. In this paper, we extend the application of ISOKANN to the CME operator and apply it to two small GRNs: a genetic toggle switch model and a model for macrophage polarization. Our work opens a new field of application for the ISOKANN algorithm and demonstrates the potential of this algorithm for studying large GRNs.}, language = {en} } @inproceedings{SikorskiRabbenChewleetal.2025, author = {Sikorski, Alexander and Rabben, Robert Julian and Chewle, Surahit and Weber, Marcus}, title = {Capturing the Macroscopic Behaviour of Molecular Dynamics with Membership Functions}, booktitle = {Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023}, editor = {Fackeldey, K.}, publisher = {De Gruyter}, arxiv = {http://arxiv.org/abs/2404.10523}, doi = {10.1515/9783111376776-004}, pages = {41 -- 58}, year = {2025}, abstract = {Markov processes serve as foundational models in many scientific disciplines, such as molecular dynamics, and their simulation forms a common basis for analysis. While simulations produce useful trajectories, obtaining macroscopic information directly from microstate data presents significant challenges. This paper addresses this gap by introducing the concept of membership functions being the macrostates themselves. We derive equations for the holding times of these macrostates and demonstrate their consistency with the classical definition. Furthermore, we discuss the application of the ISOKANN method for learning these quantities from simulation data. In addition, we present a novel method for extracting transition paths based on the ISOKANN results and demonstrate its efficacy by applying it to simulations of the 𝜇-opioid receptor. With this approach we provide a new perspective on analyzing the macroscopic behaviour of Markov systems.}, language = {en} }