TY - JOUR A1 - Chewle, Surahit A1 - Weber, Marcus A1 - Emmerling, Franziska T1 - Revealing Kinetics of Paracetamol Crystallization Using Time-Resolved Raman Spectroscopy, Orthogonal Time-Lapse Photography, and Non-Negative Matrix Factorization (OSANO) JF - Crystal Growth & Design N2 - Crystallization is a complex phenomenon with far-reaching implications for the production and formulation of active pharmaceutical ingredients. Understanding this process is critical for achieving control over key physicochemical properties that can affect, for example, the bioavailability and stability of a drug. In this study, we were able to reveal intricate and diverse dynamics of the formation of metastable intermediates of paracetamol crystallization varying with the choice of solvent. We demonstrate the efficacy of our novel approach utilizing an objective function-based non-negative matrix factorization technique for the analysis of time-resolved Raman spectroscopy data, in conjunction with time-lapse photography. Furthermore, we emphasize the crucial importance of integrating Raman spectroscopy with supplementary experimental instrumentation for the mathematical analysis of the obtained spectra. Y1 - 2023 U6 - https://doi.org/10.1021/acs.cgd.3c00617 VL - 23 IS - 9 SP - 6737 EP - 6746 PB - American Chemical Society ET - Crystal Growth & Design ER - TY - JOUR A1 - Marco, Reidelbach A1 - Marcus, Weber T1 - From Documentation to Discovery - MaRDMO and the MaRDI Ecosystem JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16735889 ER - TY - CHAP A1 - Reidelbach, Marco A1 - Schembera, Björn A1 - Weber, Marcus T1 - Towards a FAIR Documentation of Workflows and Models in Applied Mathematics T2 - Mathematical Software – ICMS 2024 N2 - 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. Y1 - 2024 U6 - https://doi.org/10.1007/978-3-031-64529-7_27 VL - 14749 SP - 254 EP - 262 PB - Springer Nature Switzerland CY - Cham ET - Lecture Notes in Computer Science ER - TY - JOUR A1 - Raharinirina, Nomenjanahary Alexia A1 - Sunkara, Vikram A1 - von Kleist, Max A1 - Fackeldey, Konstantin A1 - Weber, Marcus T1 - Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables JF - PLOS ONE Y1 - 2024 U6 - https://doi.org/10.1371/journal.pone.0302425 VL - 19 IS - 5 PB - Public Library of Science ER - TY - JOUR A1 - Reidelbach, Marco A1 - Ferrer, Eloi A1 - Weber, Marcus T1 - MaRDMO Plugin BT - Document and Retrieve Workflows Using the MaRDI Portal JF - Proceedings of the Conference on Research Data Infrastructure N2 - MaRDMO, a plugin for the Research Data Management Organiser, was developed in the Mathematical Research Data Initiative to document interdisciplinary workflows using a standardised scheme. Interdisciplinary workflows recorded this way are published directly on the MaRDI portal. In addition, central information is integrated into the MaRDI knowledge graph. Next to the documentation, MaRDMO offers the possibility to retrieve existing interdisciplinary workflows from the MaRDI Knowledge Graph to allow the reproduction of the initial work and to provide scientists with new researchimpulses. Thus, MaRDMO creates a community-driven knowledge loop that could help to overcome the replication crisis. Y1 - 2023 U6 - https://doi.org/10.52825/cordi.v1i.254 SN - 2941-296X VL - 1 PB - TIB Open Publishing ER - TY - JOUR A1 - Klasse, Sarah Michelle A1 - Weber, Marcus T1 - A Case Study of the Use of Logical Data Analysis in the Workmen’s Village in Tell el-Amarna, Egypt JF - it - Information Technology N2 - The search for patterns in archaeological contexts is crucial to understanding ancient civilizations, including the organization of their societies and economies. This study focuses on the interdisciplinary application of mathematical logical data analysis to Egyptology, particularly examining food production in the Workmen’s Village of Amarna. The introduction provides historical context about Amarna, founded by Pharaoh Akhenaten, and outlines the settlement structure and excavations of the Workmen’s Village. The research utilizes archaeobotanical and archaeological data, along with digital humanities tools, to identify activity areas related to food production. The findings reveal spatial patterns in the distribution of tools such as mortars, pestles, and ovens, providing insights into the self-sufficiency and daily life of the villagers. Logical data analysis helps in finding rules and patterns in a dataset which are not visible otherwise, predicting the distribution of unexcavated areas and thus in this case enhancing our understanding of ancient Egyptian food production practices in state- planned settlements. Y1 - 2025 U6 - https://doi.org/10.1515/itit-2024-0088 VL - 66 IS - 6 SP - 256 EP - 268 PB - de Gruyter ER - TY - JOUR A1 - Donati, Luca A1 - Chewle, Surahit A1 - St. Pierre, Dominik A1 - Natarajan, Vijay A1 - Weber, Marcus T1 - Topological analysis reveals multiple pathways in molecular dynamics JF - Journal of Chemical Theory and Computation N2 - Molecular Dynamics simulations are indispensable tools for comprehending the dynamic behavior of biomolecules, yet extracting meaningful molecular pathways from these simulations remains challenging due to the vast amount of high dimensional data. In this work, we present Molecular Kinetics via Topology (MoKiTo), a novel approach that combines the ISOKANN algorithm to determine the membership function of a molecular system with a topological analysis tool inspired by the Mapper algorithm. Our strategy efficiently identifies and characterizes distinct molecular pathways, enabling the detection and visualization of critical conformational transitions and rare events. This method offers deeper insights into molecular mechanisms, facilitating the design of targeted interventions in drug discovery and protein engineering. Y1 - 2025 U6 - https://doi.org/10.1021/acs.jctc.5c00819 VL - 21 IS - 20 SP - 10385 EP - 10397 CY - J. Chem. Theory Comput. ER - TY - JOUR A1 - Bauer, Wolfgang A1 - Weber, Marcus A1 - Diehl-Wiesenecker, Eva A1 - Galtung, Noa A1 - Prpic, Monika A1 - Somasundaram, Rajan A1 - Tauber, Rudolf A1 - Schwenk, Jochen A1 - Micke, Patrick A1 - Kappert, Kai T1 - Plasma Proteome Fingerprints Reveal Distinctiveness and Clinical Outcome of SARS-CoV-2 Infection JF - Viruses N2 - We evaluated how plasma proteomic signatures in patients with suspected COVID-19 can unravel the pathophysiology, and determine kinetics and clinical outcome of the infection. We identified distinct plasma proteins linked to the presence and course of COVID-19. These plasma proteomic findings may translate to a protein fingerprint, helping to assist clinical management decisions. Y1 - 2021 U6 - https://doi.org/10.3390/v13122456 VL - 13 IS - 12 SP - 2456 ER - TY - JOUR A1 - Kontou, Eftychia E. A1 - Walter, Axel A1 - Alka, Oliver A1 - Pfeuffer, Julianus A1 - Sachsenberg, Timo A1 - Mohite, Omkar A1 - Nuhamunanda, Matin A1 - Kohlbacher, Oliver A1 - Weber, Tilmann T1 - UmetaFlow: An untargeted metabolomics workflow for high-throughput data processing and analysis JF - Journal of Cheminformatics N2 - Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. Y1 - 2023 U6 - https://doi.org/10.1186/s13321-023-00724-w VL - 15 ER - TY - JOUR A1 - Donati, Luca A1 - Weber, Marcus T1 - Efficient Estimation of Transition Rates as Functions of pH JF - Proceedings in Applied Mathematics & Mechanics N2 - Extracting the kinetic properties of a system whose dynamics depend on the pH of the environment with which it exchanges energy and atoms requires sampling the Grand Canonical Ensemble. As an alternative, we present a novel strategy that requires simulating only the most recurrent Canonical Ensembles that compose the Grand Canonical Ensemble. The simulations are used to estimate the Gran Canonical distribution for a specific pH value by reweighting and to construct the transition rate matrix by discretizing the Fokker-Planck equation by Square Root Approximation and robust Perron Cluster Cluster Analysis. As an application, we have studied the tripeptide Ala-Asp-Ala. Y1 - 2023 U6 - https://doi.org/10.1002/pamm.202300264 VL - 23 ER - TY - JOUR A1 - Donati, Luca A1 - Weber, Marcus A1 - Keller, Bettina G. T1 - Markov models from the square root approximation of the Fokker–Planck equation: calculating the grid-dependent flux JF - Journal of Physics: Condensed Matter N2 - Molecular dynamics (MD) are extremely complex, yet understanding the slow components of their dynamics is essential to understanding their macroscopic properties. To achieve this, one models the MD as a stochastic process and analyses the dominant eigenfunctions of the associated Fokker–Planck operator, or of closely related transfer operators. So far, the calculation of the discretized operators requires extensive MD simulations. The square-root approximation of the Fokker–Planck equation is a method to calculate transition rates as a ratio of the Boltzmann densities of neighboring grid cells times a flux, and can in principle be calculated without a simulation. In a previous work we still used MD simulations to determine the flux. Here, we propose several methods to calculate the exact or approximate flux for various grid types, and thus estimate the rate matrix without a simulation. Using model potentials we test computational efficiency of the methods, and the accuracy with which they reproduce the dominant eigenfunctions and eigenvalues. For these model potentials, rate matrices with up to $\mathcal{O}\left(1{0}^{6}\right)$ states can be obtained within seconds on a single high-performance compute server if regular grids are used. Y1 - 2021 U6 - https://doi.org/10.1088/1361-648X/abd5f7 VL - 33 IS - 11 SP - 115902 ER - TY - JOUR A1 - Yousefian, Maryam A1 - Frank, Anne-Simone A1 - Weber, Marcus A1 - Röblitz, Susanna T1 - Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition JF - BMC Bioinformatics N2 - The dynamics of many gene regulatory networks (GRNs) is characterized by the occurrence of metastable phenotypes and stochastic phenotype switches. The chemical master equation (CME) is the most accurate description to model such stochastic dynamics, whereby the long-time dynamics of the system is encoded in the spectral properties of the CME operator. Markov State Models (MSMs) provide a general framework for analyzing and visualizing stochastic multistability and state transitions based on these spectral properties. Until now, however, this approach is either limited to low-dimensional systems or requires the use of high-performance computing facilities, thus limiting its usability. Y1 - 2025 U6 - https://doi.org/10.1186/s12859-025-06174-5 VL - 26 IS - 147 ER - TY - CHAP A1 - Yousefian, Maryam A1 - Donati, Luca A1 - Sikorski, Alexander A1 - Weber, Marcus A1 - Röblitz, Susanna T1 - Exploring Metastable Dynamics of Gene Regulatory Networks with ISOKANN T2 - Computational Methods in Systems Biology. CMSB 2025 N2 - 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. Y1 - 2026 U6 - https://doi.org/10.1007/978-3-032-01436-8_8 VL - 15959 SP - 126 EP - 149 ER - TY - JOUR A1 - Bauer, Wolfgang A1 - Galtung, Noa A1 - Geserick, Peter A1 - Friedrich, Katharina A1 - Weber, Marcus A1 - Somasundaram, Rajan A1 - Diehl-Wiesenecker, Eva A1 - Kappert, Kai T1 - Pentraxin-3, MyD88, GLP-1, and PD-L1: Performance assessment and composite algorithmic analysis for sepsis identification JF - Journal of Infection N2 - This study examines nine emerging biomarkers as possible indicators for diagnosing sepsis in emergency department patient. Y1 - 2025 U6 - https://doi.org/10.1016/j.jinf.2025.106599 VL - 91 IS - 3 SP - 106599 ER - TY - JOUR A1 - Coomber, Celvic A1 - Chewle, Surahit A1 - Secker, Christopher A1 - Fackeldey, Konstantin A1 - Weber, Marcus A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Investigating Endogenous Opioids Unravels the Mechanisms Behind Opioid-Induced Constipation, a Mathematical Modeling Approach JF - International Journal of Molecular Sciences N2 - Endogenous opioids, such as Endomorphin-2, are not typically associated with severe constipation, unlike pharmaceutical opioids, which induce opioid-induced constipation (OIC) by activating μ-opioid receptors in the gastrointestinal tract. In this study, we present a mathematical model, which integrates the serotonergic and opioid pathways, simulating the interaction between serotonin and opioid signaling within the enteric nervous system (ENS). The model explores the mechanisms underlying OIC, with a focus on the change in adenylyl cyclase (AC) activity, cAMP accumulation, and the distinct functionalities of Endomorphin-2 compared to commonly used pharmaceutical opioids. We study the effects of Morphine, Fentanyl, and Methadone and contrast them with Endomorphin-2. Our findings reveal that opioids do not perturb the signaling of serotonin, but only the activity of AC, suggesting that serotonin levels have no influence on improving opioid-induced constipation. Furthermore, this study reveals that the primary difference between endogenous and pharmaceutical opioids is their degradation rates. This finding shows that modulating opioid degradation rates significantly improves cAMP recovery. In conclusion, our insights steer towards exploring opioid degrading enzymes, localized to the gut, as a strategy for mitigating OIC. Y1 - 2025 U6 - https://doi.org/10.3390/ijms26136207 VL - 26 IS - 13 ER - TY - GEN A1 - Raharinirina, N. Alexia A1 - Weber, Marcus A1 - Birk, Ralph A1 - Fackeldey, Konstantin A1 - Klasse, Sarah M. A1 - Richter, Tonio Sebastian T1 - Different Tools and Results for Correspondence Analysis N2 - This is a list of codes generated from ancient egyptian texts. The codes are used for a correspondence analysis (CA). Codes and CA software are available from the linked webpage. Y1 - 2021 U6 - https://doi.org/10.12752/8257 N1 - A detailed description of the software can be found in the code repository at https://github.com/AlexiaNomena/Correspondence_Analysis_User_Friendly (repository version of CA software might include updates). ER - TY - JOUR A1 - Fernandes, Rita A1 - Chaowdhary, Suvrat A1 - Saleh, Noureldin A1 - Mikula, Natalia A1 - Kanevche, Katerina A1 - Berlepsch, Hans A1 - Hosogi, Naoki A1 - Heberle, Joachim A1 - Weber, Marcus A1 - Böttcher, Christoph A1 - Koksch, Beate T1 - Cyanine Dye Coupling Mediates Self-assembly of a pH Sensitive Peptide into Novel 3D Architectures JF - Angewandte Chemie N2 - A conjugated Cy5 dye-peptide system reveals the formation of two novel and structurally distinct supramolecular assemblies with photo-physical characteristics of H-type dimers or tetramers, respectively. The molecular ultrastructures are triggered by the complementary interplay of mutual chromophore coupling and pH induced changes in the peptide charge pattern. Y1 - 2022 VL - 61 IS - 48 SP - e202208647 ER - TY - JOUR A1 - Secker, Christopher A1 - Fackeldey, Konstantin A1 - Weber, Marcus A1 - Ray, Sourav A1 - Gorgulla, Christoph A1 - Schütte, Christof T1 - Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists JF - Journal of Cheminformatics N2 - Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the μ-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported β-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale. Y1 - 2023 U6 - https://doi.org/10.1186/s13321-023-00746-4 VL - 15 ER - TY - JOUR A1 - Sikorski, Alexander A1 - Ribera Borrell, Enric A1 - Weber, Marcus T1 - Learning Koopman eigenfunctions of stochastic diffusions with optimal importance sampling and ISOKANN JF - Journal of Mathematical Physics N2 - The dominant eigenfunctions of the Koopman operator characterize the metastabilities and slow-timescale dynamics of stochastic diffusion processes. In the context of molecular dynamics and Markov state modeling, they allow for a description of the location and frequencies of rare transitions, which are hard to obtain by direct simulation alone. In this article, we reformulate the eigenproblem in terms of the ISOKANN framework, an iterative algorithm that learns the eigenfunctions by alternating between short burst simulations and a mixture of machine learning and classical numerics, which naturally leads to a proof of convergence. We furthermore show how the intermediate iterates can be used to reduce the sampling variance by importance sampling and optimal control (enhanced sampling), as well as to select locations for further training (adaptive sampling). We demonstrate the usage of our proposed method in experiments, increasing the approximation accuracy by several orders of magnitude. Y1 - 2024 U6 - https://doi.org/10.1063/5.0140764 VL - 65 SP - 013502 ER - TY - JOUR A1 - Sikorski, Alexander A1 - Weber, Marcus A1 - Schütte, Christof T1 - The Augmented Jump Chain JF - Advanced Theory and Simulations N2 - Modern methods of simulating molecular systems are based on the mathematical theory of Markov operators with a focus on autonomous equilibrated systems. However, non-autonomous physical systems or non-autonomous simulation processes are becoming more and more important. A representation of non-autonomous Markov jump processes is presented as autonomous Markov chains on space-time. Augmenting the spatial information of the embedded Markov chain by the temporal information of the associated jump times, the so-called augmented jump chain is derived. The augmented jump chain inherits the sparseness of the infinitesimal generator of the original process and therefore provides a useful tool for studying time-dependent dynamics even in high dimensions. Furthermore, possible generalizations and applications to the computation of committor functions and coherent sets in the non-autonomous setting are discussed. After deriving the theoretical foundations, the concepts with a proof-of-concept Galerkin discretization of the transfer operator of the augmented jump chain applied to simple examples are illustrated. Y1 - 2021 U6 - https://doi.org/10.1002/adts.202000274 VL - 4 IS - 4 PB - Wiley-VCH ER -