Publications of PN 2

T. Giess and J. Pleiss, “Digitalization of biocatalysis: Best practices to research data management,” in Methods in Enzymology. , Academic Press, 2025. doi: https://doi.org/10.1016/bs.mie.2025.01.040.

M. B. M. Spera, S. Darouich, J. Pleiss, and N. Hansen, “Influence of water content on thermophysical properties of aqueous glyceline solutions predicted by molecular dynamics simulations,” Fluid Phase Equilibria, vol. 592, p. 114324, 2025, doi: https://doi.org/10.1016/j.fluid.2024.114324.

S. Ramedani, E. Kelesoglu, N. Stutzig, H. Von Tengg‐Kobligk, K. Daneshvar Ghorbani, and T. Siebert, “Quantification of training‐induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females,” Physiological Reports, vol. 13, Art. no. 3, Jan. 2025, doi: 10.14814/phy2.70187.

R. Blickhan and T. Siebert, “Note on hydrostatic skeletons: Muscles operating within a pressurized environment,” Biology Open, p. bio, May 2024, doi: 10.1242/bio.060318.

S. Weirich, D. Kusevic, P. Schnee, J. Reiter, J. Pleiss, and A. Jeltsch, “Discovery of NSD2 non-histone substrates and design of a super-substrate,” Communications Biology, vol. 7, Art. no. 1, 2024, doi: 10.1038/s42003-024-06395-z.

F. Kempter, L. Lantella, N. Stutzig, J. Fehr, and T. Siebert, “Role of Rotated Head Postures on Volunteer Kinematics and Muscle Activity in Braking Scenarios Performed on a Driving Simulator,” Annals of Biomedical Engineering, vol. 51, pp. 771–782, Apr. 2024, doi: 10.1007/s10439-022-03087-9.

T. B. Beigl et al., “BCL-2 and BOK regulate apoptosis by interaction of their C-terminal transmembrane domains,” EMBO Reports, vol. 25, Art. no. 9, 2024, doi: 10.1038/s44319-024-00206-6.

F. Neubauer, P. Bredl, M. Xu, K. Patel, J. Pleiss, and B. Uekermann, “MetaConfigurator: A User-Friendly Tool for Editing Structured Data Files,” Datenbank-Spektrum, vol. 24, pp. 161–169, 2024, doi: 10.1007/s13222-024-00472-7.

A. S. Sahrmann, L. Vosse, T. Siebert, G. G. Handsfield, and O. Röhrle, “3D ultrasound-based determination of skeletal muscle fascicle orientations,” Biomechanics and Modeling in Mechanobiology, Mar. 2024, doi: 10.1007/s10237-024-01837-3.

A. Windels, J. Franceus, J. Pleiss, and T. Desmet, “CANDy: Automated analysis of domain architectures in carbohydrate-active enzymes,” PLOS ONE, vol. 19, Art. no. 7, Jul. 2024, doi: 10.1371/journal.pone.0306410.

C. Coenning, V. Rieg, T. Siebert, and V. Wank, “Impact of contraction intensity and ankle joint angle on calf muscle fascicle length and pennation angle during isometric and dynamic contractions,” Scientific Reports, vol. 14, Art. no. 1, Oct. 2024, doi: 10.1038/s41598-024-75795-2.

M. Millard, N. Stutzig, J. Fehr, and T. Siebert, “A benchmark of muscle models to length changes great and small,” Journal of the Mechanical Behavior of Biomedical Materials, vol. 160, p. 106740, 2024, doi: 10.1016/j.jmbbm.2024.106740.

S. M. Seyedpour, M. Azhdari, L. Lambers, T. Ricken, and G. Rezazadeh, “One-dimensional thermomechanical bio-heating analysis of viscoelastic tissue to laser radiation shapes,” International Journal of Heat and Mass Transfer, vol. 218, p. 124747, 2024, doi: https://doi.org/10.1016/j.ijheatmasstransfer.2023.124747.

P. Carlo Danesini, M. Heim, A. Tomalka, T. Siebert, and F. Ates, “Endomysium determines active and passive force production in muscle fibers,” Journal of Biomechanics, vol. 168, p. 112134, May 2024, doi: 10.1016/j.jbiomech.2024.112134.

A. S. Sahrmann, L. Vosse, T. Siebert, G. G. Handsfield, and O. Röhrle, “Determination of muscle shape deformations of the tibialis anterior during dynamic contractions using 3D ultrasound,” Frontiers in Bioengineering and Biotechnology, vol. 12, 2024, doi: 10.3389/fbioe.2024.1388907.

J. Pleiss, “FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science,” ACS Catal., vol. 14, Art. no. 4, Feb. 2024, doi: 10.1021/acscatal.3c06337.

S. Malzacher et al., “The STRENDA Biocatalysis Guidelines for cataloguing metadata,” Nature Catalysis, vol. 7, Art. no. 12, 2024, doi: 10.1038/s41929-024-01261-x.

S. Weidner, A. Tomalka, C. Rode, and T. Siebert, “Impact of lengthening velocity on the generation of eccentric force by slow-twitch muscle fibers in long stretches,” Pfluegers Archiv - European Journal of Physiology, Jul. 2024, doi: 10.1007/s00424-024-02991-4.

J. Pleiss, “Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis,” Biochemistry, vol. 63, Art. no. 20, Sep. 2024, doi: 10.1021/acs.biochem.4c00501.

A. Tomalka, S. Weidner, D. Hahn, W. Seiberl, and T. Siebert, “Force re-development after shortening reveals a role for titin in stretch-shortening performance enhancement in skinned muscle fibres,” Journal of Experimental Biology, Aug. 2024, doi: 10.1242/jeb.247377.

K. Entrop, S. Wieske, and M. Rehm, “Why Bax detection in >1400 publications might be flawed,” Cell Death & Disease, vol. 15, Art. no. 12, Dec. 2024, doi: 10.1038/s41419-024-07273-6.

B. Flemisch et al., “Research Data Management in Simulation Science: Infrastructure, Tools, and Applications,” Datenbank-Spektrum, 2024, doi: https://doi.org/10.1007/s13222-024-00475-4.

P. Schnee, J. Pleiss, and A. Jeltsch, “Approaching the catalytic mechanism of protein lysine methyltransferases by biochemical and simulation techniques,” Critical Reviews in Biochemistry and Molecular Biology, vol. 59, Art. no. 1–2, 2024, doi: 10.1080/10409238.2024.2318547.

E. Scherff, S. E. Schnell, T. Siebert, and S. D’Souza, “Reference measures of lower-limb joint range of motion, muscle strength, and selective voluntary motor control of typically developing children aged 5–17 years,” Journal of Children’s Orthopaedics, pp. 1–10, May 2024, doi: 10.1177/18632521241234768.

S. M. Seyedpour, L. Lambers, G. Rezazadeh, and T. Ricken, “Mathematical modelling of the dynamic response of an implantable enhanced capacitive glaucoma pressure sensor,” Measurement: Sensors, p. 100936, 2023, doi: https://doi.org/10.1016/j.measen.2023.100936.

C. Guttà, C. Morhard, and M. Rehm, “Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer,” PLOS Computational Biology, vol. 19, Art. no. 4, Apr. 2023, doi: 10.1371/journal.pcbi.1011035.

S. Lauterbach et al., “EnzymeML: seamless data flow and modeling of enzymatic data,” Nature Methods, vol. 20, Art. no. 3, 2023, doi: 10.1038/s41592-022-01763-1.

M. S. Khella et al., “The T1150A cancer mutant of the protein lysine dimethyltransferase NSD2 can introduce H3K36 trimethylation,” Journal of Biological Chemistry, vol. 299, Art. no. 6, 2023, doi: https://doi.org/10.1016/j.jbc.2023.104796.

M. Azhdari et al., “Non-local three phase lag bio thermal modeling of skin tissue and experimental evaluation,” International Communications in Heat and Mass Transfer, vol. 149, p. 107146, 2023, doi: https://doi.org/10.1016/j.icheatmasstransfer.2023.107146.

R. Christian, T. Andre, B. R., and S. Tobias, “Structurally motivated models to explain the muscle’s force-length relationship,” Biophysical Journal 1, vol. 122, Art. no. 17, Sep. 2023, doi: 10.1016/j.bpj.2023.05.026.

L. Pfitzer, J. Heitkämper, J. Kästner, and R. Peters, “Use of the N–O Bonds in N-Mesyloxyamides and N-Mesyloxyimides To Gain Access to 5-Alkoxy-3,4-dialkyloxazol-2-ones and 3-Hetero-Substituted Succinimides: A Combined Experimental and Theoretical Study,” Synthesis, vol. 55, Art. no. 26, 2023, doi: 10.1055/s-0042-1751447.

L. Mandl, A. Mielke, S. M. Seyedpour, and T. Ricken, “Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem,” Scientific Reports, vol. 13, Art. no. 1, 2023, doi: 10.1038/s41598-023-42141-x.

V. Wagner and N. Radde, “The impossible challenge of estimating non-existent moments of the Chemical Master Equation,” Bioinformatics, vol. 39, Art. no. Supplement_1, Jun. 2023, doi: 10.1093/bioinformatics/btad205.

T. Giess, S. Itzigehl, J. Range, R. Schömig, J. R. Bruckner, and J. Pleiss, “FAIR and scalable management of small-angle X-ray scattering data,” Journal of Applied Crystallography, vol. 56, Art. no. 2, Apr. 2023, doi: 10.1107/S1600576723001577.

K. Gubaev, V. Zaverkin, P. Srinivasan, A. I. Duff, J. Kästner, and B. Grabowski, “Performance of two complementary machine-learned potentials in modelling chemically complex systems,” NPJ Computational Materials, vol. 9, p. 129, 2023, doi: 10.1038/s41524-023-01073-w.

V. Wagner, R. Strässer, F. Allgöwer, and N. E. Radde, “A provably convergent control closure scheme for the Method of Moments of the Chemical Master Equation,” Journal of Chemical Theory and Computation, vol. 19, Art. no. 24, Dec. 2023, doi: https://doi.org/10.1021/acs.jctc.3c00548.

S. Höpfl, J. Pleiss, and N. E. Radde, “Bayesian estimation reveals that reproducible models in Systems Biology get more citations,” Scientific reports, vol. 13, p. 2695, 2023, doi: 10.1038/s41598-023-29340-2.

A. Tomalka, “Eccentric muscle contractions : from single muscle fibre to whole muscle mechanics,” Pflügers Archiv, vol. 475, pp. 421–435, 2023, doi: 10.1007/s00424-023-02794-z.

T. Walter, N. Stutzig, and T. Siebert, “Active exoskeleton reduces erector spinae muscle activity during lifting,” Frontiers in Bioengineering and Biotechnology, vol. 11, Apr. 2023, doi: 10.3389/fbioe.2023.1150170.

C. Guttà, C. Morhard, and M. Rehm, “T-GAN-D: a GAN-based classifier for breast cancer prognostication,” Oct. 2022, doi: 10.5281/zenodo.7151831.

S. Weidner, A. Tomalka, C. Rode, and T. Siebert, “How velocity impacts eccentric force generation of fully activated skinned skeletal muscle fibers in long stretches,” Journal of Applied Physiology, vol. 133, Art. no. 1, 2022, doi: 10.1152/japplphysiol.00735.2021.

A. H. Ludwig-Słomczyńska and M. Rehm, “Mitochondrial genome variations, mitochondrial-nuclear compatibility, and their association with metabolic diseases,” Obesity, May 2022, doi: 10.1002/OBY.23424.

J. Vera et al., “Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence,” Briefings in Bioinformatics, Oct. 2022, doi: 10.1093/BIB/BBAC433.

F. Bertrand, M. Brodbeck, and T. Ricken, “On robust discretization methods for poroelastic problems: Numerical examples and counter-examples,” Examples and Counterexamples, vol. 2, p. 100087, Nov. 2022, doi: 10.1016/j.exco.2022.100087.

C. Hagenlocher, R. Siebert, B. Taschke, S. Wieske, A. Hausser, and M. Rehm, “ER stress-induced cell death proceeds independently of the TRAIL-R2 signaling axis in pancreatic β cells,” Cell Death Discovery, vol. 8, Art. no. 1, Jan. 2022, doi: 10.1038/s41420-022-00830-y.

C. Boccellato and M. Rehm, “Glioblastoma, from disease understanding towards optimal cell-based in vitro models,” Cellular Oncology, Jun. 2022, doi: 10.1007/s13402-022-00684-7.

V. Wagner, S. Höpfl, V. Klingel, M. C. Pop, and N. E. Radde, “An inverse transformation algorithm to infer parameter distributions from population snapshot data,” IFAC-PapersOnLine, vol. 55, Art. no. 23, 2022, doi: https://doi.org/10.1016/j.ifacol.2023.01.020.

A. Tomalka, M. Heim, A. Klotz, C. Rode, and T. Siebert, “Ultrastructural and kinetic evidence support that thick filaments slide through the Z-disc,” Interface : journal of the Royal Society, vol. 19, Art. no. 197, 2022, doi: 10.1098/rsif.2022.0642.

A. Mack et al., “Preferential Self-interaction of DNA Methyltransferase DNMT3A Subunits Containing the R882H Cancer Mutation Leads to Dominant Changes of Flanking Sequence Preferences,” Journal of Molecular Biology, vol. 434, Art. no. 7, 2022, doi: 10.1016/j.jmb.2022.167482.

V. Wagner, B. Castellaz, M. Oesting, and N. Radde, “Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation,” Bioinformatics, vol. 38, Art. no. 18, 2022, doi: 10.1093/bioinformatics/btac501.

P. Schnee et al., “Mechanistic basis of the increased methylation activity of the SETD2 protein lysine methyltransferase towards a designed super-substrate peptide,” Communications Chemistry, vol. 5, Art. no. 1, 2022, doi: 10.1038/s42004-022-00753-w.

F. Kempter, L. Lantella, N. Stutzig, J. Fehr, and T. Siebert, “Role of Rotated Head Postures on Volunteer Kinematics and Muscle Activity in Braking Scenarios Performed on a Driving Simulator,” Annals of Biomedical Engineering, vol. 51, Art. no. 4, 2022, doi: 10.1007/s10439-022-03087-9.

J. Range et al., “EnzymeML—a data exchange format for biocatalysis and enzymology,” The FEBS Journal, vol. 289, Art. no. 19, Oct. 2022, doi: https://doi.org/10.1111/febs.16318.

N. Krishna Moorthy et al., “Low-Level Endothelial TRAIL-Receptor Expression Obstructs the CNS-Delivery of Angiopep-2 Functionalised TRAIL-Receptor Agonists for the Treatment of Glioblastoma,” Molecules, vol. 26, Art. no. 24, Dec. 2021, doi: 10.3390/MOLECULES26247582.

A. Armiti-Juber and T. Ricken, “Model order reduction for deformable porous materials in thin domains via asymptotic analysis,” Archive of Applied Mechanics, 2021, doi: 10.1007/s00419-021-01907-3.

S. M. Seyedpour et al., “Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review,” Frontiers in Physiology, vol. 12, Sep. 2021, doi: 10.3389/fphys.2021.733393.

C. Boccellato et al., “Marizomib sensitizes primary glioma cells to apoptosis induced by a latest-generation TRAIL receptor agonist,” Cell Death & Disease, vol. 12, Art. no. 7, Jul. 2021, doi: 10.1038/s41419-021-03927-x.

B. Christ et al., “Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function,” Frontiers in Physiology, vol. 12, Nov. 2021, doi: 10.3389/fphys.2021.733868.

W. Ehlers, M. Morrison (Rehm), P. Schröder, D. Stöhr, and A. Wagner, “Multiphasic modelling and computation of metastatic lung-cancer cell proliferation and atrophy in brain tissue based on experimental data,” Biomechanics and Modeling in Mechanobiology, 2021, doi: 10.1007/s10237-021-01535-4.

J. Pleiss, “Standardized data, scalable documentation, sustainable storage – EnzymeML as a basis for FAIR data management in biocatalysis,” ChemCatChem, vol. 13, pp. 3909–3913, 2021, doi: https://doi.org/10.1002/cctc.202100822.

A. Tomalka, S. Weidner, D. Hahn, W. Seiberl, and T. Siebert, “Power Amplification Increases With Contraction Velocity During Stretch-Shortening Cycles of Skinned Muscle Fibers,” Frontiers in Physiology, vol. 12, Mar. 2021, doi: 10.3389/fphys.2021.644981.

K. Kuritz, D. Stöhr, D. S. Maichl, N. Pollak, M. Rehm, and F. Allgöwer, “Reconstructing temporal and spatial dynamics from single-cell pseudotime using prior knowledge of real scale cell densities,” Scientific Reports, vol. 10, Art. no. 1, Dec. 2020, doi: 10.1038/s41598-020-60400-z.

D. Imig, N. Pollak, F. Allgöwer, and M. Rehm, “Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis,” PLOS Computational Biology, vol. 16, Art. no. 6, Jun. 2020, doi: 10.1371/journal.pcbi.1007812.

S. Adam et al., “DNA sequence-dependent activity and base flipping mechanisms of DNMT1 regulate genome-wide DNA methylation,” Nature Communications, vol. 11, Art. no. 1, 2020, doi: 10.1038/s41467-020-17531-8.

D. Stöhr et al., “Stress-induced TRAILR2 expression overcomes TRAIL resistance in cancer cell spheroids,” Cell Death & Differentiation, Art. no. 27, 2020, doi: 10.1038/s41418-020-0559-3.

C. Guttà et al., “Low expression of pro-apoptotic proteins Bax, Bak and Smac indicates prolonged progression-free survival in chemotherapy-treated metastatic melanoma,” Cell Death & Disease, vol. 11, Art. no. 2, Feb. 2020, doi: 10.1038/s41419-020-2309-3.

D. Stöhr, A. Jeltsch, and M. Rehm, “TRAIL receptor signaling: From the basics of canonical signal transduction toward its entanglement with ER stress and the unfolded protein response.,” Cell Death Regulation in Health and Disease-Part A, p. 57, 2020.

A. Tomalka, S. Weidner, D. Hahn, W. Seiberl, and T. Siebert, “Cross-Bridges and Sarcomeric Non-cross-bridge Structures Contribute to Increased Work in Stretch-Shortening Cycles,” Frontiers in Physiology, vol. 11, Jul. 2020, doi: 10.3389/fphys.2020.00921.

G. Fullstone, T. L. Bauer, C. Guttà, M. Salvucci, J. H. M. Prehn, and M. Rehm, “The apoptosome molecular timer synergises with XIAP to suppress apoptosis execution and contributes to prognosticating survival in colorectal cancer,” Cell Death & Differentiation, Art. no. 27, 2020, doi: 10.1038/s41418-020-0545-9.

V. Vetma et al., “Convergence of pathway analysis and pattern recognition predicts sensitization to latest generation TRAIL therapeutics by IAP antagonism,” Cell Death & Differentiation, vol. 27, Art. no. 8, Feb. 2020, doi: 10.1038/s41418-020-0512-5.

D. Stöhr and M. Rehm, “Linking hyperosmotic stress and apoptotic sensitivity,” The FEBS Journal, p. febs, Aug. 2020, doi: 10.1111/febs.15520.

G. Fullstone, C. Guttà, A. Beyer, and M. Rehm, “The FLAME-accelerated signalling tool (FaST) for facile parallelisation of flexible agent-based models of cell signalling,” npj Systems Biology and Applications, vol. 6, Art. no. 1, 2020, doi: 10.1038/s41540-020-0128-x.

A. Tomalka, O. Röhrle, J. C. Han, T. Pham, A. J. Taberner, and T. Siebert, “Extensive eccentric contractions in intact cardiac trabeculae: revealing compelling differences in contractile behaviour compared to skeletal muscles,” Proceedings of the Royal Society B, vol. 286, Art. no. 1903, 2019, doi: 10.1098/rspb.2019.0719.

P. L. Reiser, J. E. Aguilar, A. Guthke, and P.-C. Bürkner, “Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference.” 2024. doi: 10.18419/darus-4093.

P. F. Santana Chacon, M. Hammer, I. Wochner, J. R. Walter, and S. Schmitt, “Replication Data for: A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers.” 2023. doi: 10.18419/darus-3796.

F. Kempter, L. Lantella, N. Stutzig, J. C. Fehr, and T. Siebert, “Neck Reflex Behavior in Driving Simulator Experiments - Academic-Scale Simulator at ITM.” 2023. doi: 10.18419/darus-3000.

J. Kneifl, J. Hay, and J. Fehr, “Human Occupant Motion in Pre-Crash Scenario.” 2022. doi: 10.18419/darus-2471.

S. Schmitt, “demoa-base: a biophysics simulator for muscle-driven motion.” 2022. doi: 10.18419/darus-2550.

I. Wochner and S. Schmitt, “MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks.” 2022. doi: 10.18419/darus-3268.

M. Hammer, J. M. Riede, L. Meszaros-Beller, and S. Schmitt, “gspine: A Human Spine Model Built Using Literature Data : a human spine model built using literature data.” 2022. doi: 10.18419/darus-2814.