Publications of PN 2

  1. 2022

    1. 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.
    2. 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.
    3. V. Klingel, D. Graf, S. Weirich, A. Jeltsch, and N. E. Radde, “Model-Based Design of a Synthetic Oscillator Based on an Epigenetic Methylation Memory System,” ACS Synthetic Biology, vol. 11, no. 7, Art. no. 7, Jun. 2022, doi: 10.1021/acssynbio.2c00118.
    4. 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, no. 1, Art. no. 1, Jan. 2022, doi: 10.1038/s41420-022-00830-y.
    5. C. Guttà, C. Morhard, and M. Rehm, “Applying GAN-based data augmentation to improve transcriptome-based prognostication in breast cancer,” medRxiv, Cold Spring Harbor Laboratory Press, Oct. 2022. doi: 10.1101/2022.10.07.22280776.
    6. C. Guttà, C. Morhard, and M. Rehm, “T-GAN-D: a GAN-based classifier for breast cancer                    prognostication.” Zenodo, Oct. 2022. doi: 10.5281/zenodo.7151831.
    7. 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.
    8. S. Adam et al., “Flanking sequences influence the activity of TET1 and TET2 methylcytosine dioxygenases and affect genomic 5hmC patterns,” Communications Biology, vol. 5, no. 1, Art. no. 1, Jan. 2022, doi: 10.1038/s42003-022-03033-4.

  2. 2021

    1. 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.” 2021.
    2. V. Wagner and N. Radde, “SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems,” Mathematics, vol. 9, p. 1074, 2021, doi: 10.3390/math9101074.
    3. 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.
    4. M. Suditsch, P. Schröder, L. Lambers, T. Ricken, W. Ehlers, and A. Wagner, “Modelling basal-cell carcinoma behaviour in avascular skin,” PAMM, vol. 20, no. 1, Art. no. 1, Jan. 2021, doi: 10.1002/pamm.202000283.
    5. M. Suditsch, L. Lambers, T. Ricken, and A. Wagner, “Application of a continuum-mechanical tumour model to brain tissue,” PAMM, vol. 21, no. 1, Art. no. 1, 2021, doi: 10.1002/pamm.202100204.
    6. 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.
    7. S. M. Seyedpour, I. Valizadeh, P. Kirmizakis, R. Doherty, and T. Ricken, “Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method,” Water, vol. 13, no. 3, Art. no. 3, 2021, doi: 10.3390/w13030383.
    8. N. Pollak et al., “Cell cycle progression and transmitotic apoptosis resistance promote escape from extrinsic apoptosis,” Journal of cell science, p. jcs.258966--, Nov. 2021, doi: 10.1242/jcs.258966.
    9. L. Lambers, M. Suditsch, A. Wagner, and T. Ricken, “A Multiscale and Multiphase Model of Function-Perfusion Growth Processes in the Human Liver,” PAMM, vol. 20, no. 1, Art. no. 1, Jan. 2021, doi: 10.1002/pamm.202000290.
    10. L. Lambers, A. Mielke, and T. Ricken, “Semi-automated Data-driven FE Mesh Generation and Inverse Parameter Identification for a Multiscale and Multiphase Model of Function-Perfusion Processes in the Liver,” PAMM, vol. 21, no. 1, Art. no. 1, 2021, doi: 10.1002/pamm.202100190.
    11. 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 2021, Vol. 26, Page 7582, vol. 26, no. 24, Art. no. 24, Dec. 2021, doi: 10.3390/MOLECULES26247582.
    12. V. Klingel, J. Kirch, T. Ullrich, S. Weirich, A. Jeltsch, and N. E. Radde, “Model-based robustness and bistability analysis for methylation-based, epigenetic memory systems,” The FEBS Journal, vol. 288, no. 19, Art. no. 19, 2021, doi: 10.1111/febs.15838.
    13. C. T. Hellwig et al., “Proteasome inhibition triggers the formation of TRAIL receptor 2 platforms for caspase-8 activation that accumulate in the cytosol,” Cell Death & Differentiation 2021, pp. 1--9, Aug. 2021, doi: 10.1038/s41418-021-00843-7.
    14. 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.
    15. 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.
    16. C. Boccellato et al., “Marizomib sensitizes primary glioma cells to apoptosis induced by a latest-generation TRAIL receptor agonist,” Cell Death & Disease, vol. 12, no. 7, Art. no. 7, Jul. 2021, doi: 10.1038/s41419-021-03927-x.
    17. F. Bertrand, L. Lambers, and T. Ricken, “Least Squares Finite Element Method for Hepatic Sinusoidal Blood Flow,” PAMM, vol. 20, no. 1, Art. no. 1, Jan. 2021, doi: 10.1002/pamm.202000306.
    18. 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.

  3. 2020

    1. 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, no. 8, Art. no. 8, Feb. 2020, doi: 10.1038/s41418-020-0512-5.
    2. 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.
    3. D. Stöhr et al., “Stress-induced TRAILR2 expression overcomes TRAIL resistance in cancer cell spheroids,” Cell Death & Differentiation, no. 27, Art. no. 27, 2020, doi: 10.1038/s41418-020-0559-3.
    4. 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.
    5. D. Stöhr and M. Rehm, “Linking hyperosmotic stress and apoptotic sensitivity,” The FEBS Journal, p. febs.15520, Aug. 2020, doi: 10.1111/febs.15520.
    6. D. Stöhr et al., “Stress-induced TRAILR2 expression overcomes TRAIL resistance in cancer cell spheroids,” Cell Death & Differentiation, pp. 1--16, 2020.
    7. 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, no. 1, Art. no. 1, Dec. 2020, doi: 10.1038/s41598-020-60400-z.
    8. 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, no. 6, Art. no. 6, Jun. 2020, doi: 10.1371/journal.pcbi.1007812.
    9. 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, no. 2, Art. no. 2, Feb. 2020, doi: 10.1038/s41419-020-2309-3.
    10. 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, no. 1, Art. no. 1, 2020, doi: 10.1038/s41540-020-0128-x.
    11. G. Fullstone, T. L. Bauer, C. Guttà, M. Salvucci, J. H. 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, pp. 1--15, 2020.
    12. 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, 2020, doi: 10.1038/s41418-020-0545-9.
    13. S. Adam et al., “DNA sequence-dependent activity and base flipping mechanisms of DNMT1 regulate genome-wide DNA methylation,” Nat. Communications, vol. 11, no. 1, Art. no. 1, 2020, doi: 10.1038/s41467-020-17531-8.

  4. 2019

    1. 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, no. 1903, Art. no. 1903, 2019.
    2. T. Ricken and L. Lambers, “On computational approaches of liver lobule function and perfusion simulation,” GAMM-Mitteilungen, vol. 42, no. 4, Art. no. 4, May 2019, doi: 10.1002/gamm.201900016.
    3. L. Lambers, T. Ricken, and M. König, “Model Order Reduction (MOR) of Function--Perfusion--Growth Simulation in the Human Fatty Liver via Artificial Neural Network (ANN),” PAMM, vol. 19, no. 1, Art. no. 1, 2019, doi: 10.1002/pamm.201900429.
    4. L. Lambers, T. Ricken, and M. König, “A multiscale and multiphase model for the description of function-perfusion processes in the human liver,” in Advances in Engineering Materials, Structures and Systems : Innovations, Mechanics and Applications : Proceedings of the 7th International Conference on Structural Engineering, Mechanics and Computation (SEMC 2019), September 2-4, 2019, Cape Town, South Africa, Cape Town, South Africa, 2019, pp. 304–307. doi: 10.1201/9780429426506-52.
    5. T. Kuhn, J. Dürrwächter, F. Meyer, A. Beck, C. Rohde, and C.-D. Munz, “Uncertainty quantification for direct aeroacoustic simulations of cavity flows,” J. Theor. Comput. Acoust., vol. 27, no. 1, 1850044, Art. no. 1, 1850044, 2019, doi: https://doi.org/10.1142/S2591728518500445.
    6. E.-M. Geissen, J. Hasenauer, and N. E. Radde, “Inference of finite mixture models and the effect of binning,” Statistical applications in genetics and molecular biology, vol. 18, no. 4, Art. no. 4, 2019.

Project Network Coordinators

This image shows Nicole Radde

Nicole Radde

Prof. Dr. rer. nat.

[Photo: SimTech/Max Kovalenko]

This image shows Oliver Röhrle

Oliver Röhrle

Prof.

[Photo: SimTech/Max Kovalenko]

To the top of the page