Computational Biomechanics

Mechanical simulations of biological tissue systems.

Why Biomechanics?

Biomechanics is a modern topic and offers an enormous range of interesting and challenging problems. Common to many biomechanical problems is a strong scale-dependent behaviour. It is important to analyze the behaviour of material at all scales and adjust models accordingly.

Almost all tissue in the human body contains collagen fibers. A typical example is articular cartilage, where collagen fibers distribute stress optimally for minimizing wear. The Theory of Porous Media (TPM) presents a framework for handling these, as well as other typical properties of biological material. Combining this framwork with the medical knowledge of research partners offers deep insight into vital biological processes in the human body.

Current projects

Articular Cartilage Simulation

Modeling and simulation of articular cartilage

Brain Tumour Modelling

Data-integrated simulation of tumour growth and regression in brain tissue

Digital Liver Lab

Multiscale and Multiphase Modeling of the Human Liver

Modelling of material injection processes into porous structures applied to vertebroplasty

Research Project C03

Publications

  1. 2026

    1. Bafna, M., König, M., Saalfeld, S., Moulisova, V., Liska, V., Dahmen, U., & Albadry, M. (2026). Automated segmentation of hepatic vessels and lobules in whole-slide images using U-net models. Frontiers in Bioinformatics, Volume 6 - 2026. https://doi.org/10.3389/fbinf.2026.1713736
    2. Azhdari, M., Kamrava, M., Rezazadeh, G., Pathak, R., Schulze-Späte, U., Ricken, T., & Seyedpour, S. M. (2026). From mechanical models to clinical reality: A systematic review of finite element advances in dental implant design, biomechanics, and optimization. Materials Today Communications, 50, 114314. https://doi.org/10.1016/j.mtcomm.2025.114314
    3. Myshkina, M., Elias, M., Tensil, E., & König, M. (2026). Reproducibility of a Digital Twin of the Angiotensin II Receptor Blocker Losartan. https://doi.org/10.36903/physiome.31359823
    4. Azhdari, M., Rezazadeh, G., Pathak, R., Tautenhahn, H.-M., Tautenhahn, F., Ricken, T., & Seyedpour, S. M. (2026). A critical review of non-Fourier heat transfer theories with phase lag in bio-heating: Explaining the variations in reported phase lag coefficients. International Journal of Thermal Sciences, 220, 110376. https://doi.org/10.1016/j.ijthermalsci.2025.110376
    5. Elias, M., Myshkina, M., Nemitz, N., & König, M. (2026). Reproducibility of a Physiologically Based Pharmacokinetic and Pharmacodynamic (PBPK/PD) Model of Dapagliflozin. https://doi.org/10.36903/physiome.31368556
    6. Tereshchuk, V., Elias, M., & König, M. (2026). A Digital Twin of Canagliflozin Pharmacokinetics and Pharmacodynamics in Type 2 Diabetes Mellitus. https://doi.org/10.20944/preprints202601.2095.v1
    7. Azhdari, M., Kamrava, M., Rezazadeh, G., Pathak, R., Schulze-Späte, U., Ricken, T., & Seyedpour, S. M. (2026). From mechanical models to clinical reality: A systematic review of finite element advances in dental implant design, biomechanics, and optimization. Materials Today Communications, 50, 114314. https://doi.org/10.1016/j.mtcomm.2025.114314
    8. Ghanbari, M., Seyedpour, S. M., & Rezazadeh, G. (2026). A predictive 2D Zener model for laser-induced pressure waves in photoacoustic imaging of human tissue. Applied Mathematical Modelling, 152, 116537. https://doi.org/10.1016/j.apm.2025.116537
    9. Alejandro, J., Elias, M., Babaeva, M., & König, M. (2026). A Digital Twin of Empagliflozin Pharmacokinetics and Pharmacodynamics. https://doi.org/10.20944/preprints202603.1559.v1
    10. Nemitz, N., Elias, M., & König, M. (2026). A Physiologically Based Pharmacokinetic and Pharmacodynamic (PBPK/PD) Model of Dapagliflozin in Type 2 Diabetes Mellitus: The Effect of Dosing, Hepatorenal Impairment, and Food. Pharmaceutics, 18, Article 3. https://doi.org/10.3390/pharmaceutics18030287
    11. Tensil, E., Myshkina, M., & König, M. (2026). A Digital Twin of the Angiotensin II Receptor Blocker Losartan: Physiologically Based Modeling of Blood Pressure Regulation. Pharmaceutics, 18, Article 2. https://doi.org/10.3390/pharmaceutics18020262

  2. 2025

    1. Baum, P., Ebert, T., Klöting, N., Krupka, S., König, M., Paeschke, S., Stock, P., Bulc, M., Blüher, M., Palus, K., Nowicki, M., & Kosacka, J. (2025). Inflammation and autophagy in peripheral nerves of rodent models with metabolic syndrome and type 2 diabetes mellitus. Neuroscience Research. https://doi.org/10.1016/j.neures.2025.04.002
    2. Suditsch, M., Egli, F. S., Lambers, L., & Ricken, T. (2025). Growth in biphasic tissue. International Journal of Engineering Science. https://doi.org/10.1016/j.ijengsci.2024.104183
    3. Suditsch, M., Wagner, A., & Ricken, T. (2025). Onco*: An umbrella Python framework for modelling and simulation of oncological scenarios. Journal of Computational Science, 85, 102533. https://doi.org/10.1016/j.jocs.2025.102533
    4. Gupta, I., Schanz, M., & Ricken, T. (2025). Thrombosis Simulation Using a Triphasic Porous Medium Model: Application to Aortic Dissection. Journal of Engineering Mechanics, 151, Article 9. https://doi.org/10.1061/JENMDT.EMENG-8468
    5. Egli, F. S., Seyedpour, S. M., Pachenari, M., Pierce, D. M., & Ricken, T. (2025). Computational modeling of articular cartilage: Mechanical experiments, sensitivity analyses, parameter identification, and validation. Acta Biomaterialia, 204, 429–445. https://doi.org/10.1016/j.actbio.2025.07.043
    6. Azhdari, M., Rezazadeh, G., Pathak, R., Tautenhahn, H.-M., Tautenhahn, F., Ricken, T., & Seyedpour, S. M. (2025). Non-Fourier bioheat transfer modeling: An extensive critical review of state of the art, caveats, and future directions. International Communications in Heat and Mass Transfer, 169, 109509. https://doi.org/10.1016/j.icheatmasstransfer.2025.109509
    7. Myshkina, M., Casabianca, E., Schnurpel, A., Ricken, T., Tautenhahn, H.-M., & König, M. (2025). Assessing the Impact of AI and Digital Twins on Clinical Decision-Making in Hepatology and Hepatobiliary Surgery. https://doi.org/10.20944/preprints202509.1164.v1
    8. Elias, M., & König, M. (2025). A Digital Twin of Glimepiride for Personalized and Stratified Diabetes Treatment. https://doi.org/10.20944/preprints202506.1264.v1
    9. Balaur, I., Nickerson, D. P., Welter, D., Wodke, J. A. H., Ancien, F., Gebhardt, T., Grouès, V., Hermjakob, H., König, M., Radde, N., Rougny, A., Schneider, R., Malik-Sheriff, R. S., Shiferaw, K. B., Stefan, M., Satagopam, V., & Waltemath, D. (2025). FAIRification of computational models in biology. https://doi.org/10.1101/2025.03.21.644517
    10. Azhdari, M., Rezazadeh, G., Ricken, T., Pathak, R., Tautenhahn, H.-M., Tautenhahn, F., & Seyedpour, S. M. (2025). Temperature distribution in multi-layered skin tissue during laser irradiation considering epidermis sublayers: Virtual Element Method approach. Thermal Science and Engineering Progress, 59, 103297. https://doi.org/10.1016/j.tsep.2025.103297
    11. Ali Mirza, Z., Azhdari, M., Kolomenskiy, D., Rezazadeh, G., Ricken, T., Pathak, R., Tautenhahn, H.-M., Tautenhahn, F., & Seyedpour, S. M. (2025). Enhancing laser therapy procedure through surface temperature control in multi-layered skin tissue. Journal of Thermal Biology, 129, 104106. https://doi.org/10.1016/j.jtherbio.2025.104106
    12. Elias, M., & König, M. (2025). Reproducibility of a Digital Twin of Glimepiride for Personalized and Stratified Diabetes Treatment. Physiome. https://doi.org/10.36903/physiome.28379193
    13. Bafna, M., König, M., Saalfeld, S., Moulisova, V., Liska, V., Dahmen, U., & Albadry, M. (2025). Automated Segmentation of Hepatic Vessels and Lobules in Whole-Slide Images Using U-Net Models. https://doi.org/10.1101/2025.09.08.674181
    14. Tahouni, S., Azhdari, M., Rezazadeh, G., Fathalilou, M., Pathak, R., Ricken, T., & Seyedpour, S. M. (2025). Experimental and numerical analysis of heat transfer in polymer composites with metallic inclusions using virtual element method. Materials & Design, 255, 114172. https://doi.org/10.1016/j.matdes.2025.114172
    15. Mandl, L., Goswami, S., Lambers, L., & Ricken, T. (2025). Separable physics-informed DeepONet : Breaking the curse of dimensionality in physics-informed machine learning. Computer Methods in Applied Mechanics and Engineering, 434, 117586.
    16. Almasi, A., Ricken, T., & Pierce, D. M. (2025). Finite elements of multiscale mixtures (FE2M) in three dimensions: theory, numerical implementation, and analyses. Computational Mechanics. https://doi.org/10.1007/s00466-025-02669-3
    17. Tensil, E., & König, M. (2025). A Digital Twin of the Angiotensin II Receptor Blocker Losartan: Physiologically Based Modeling of Blood Pressure Regulation. https://doi.org/10.20944/preprints202510.1254.v1
    18. Casabianca, E., Myshkina, M., & König, M. (2025). A Systems Pharmacology Approach to Rivaroxaban: Physiologically Based Modeling of Pharmacokinetics and Coagulation Dynamics. https://doi.org/10.20944/preprints202507.1945.v1
    19. Nemitz, N., Elias, M., & König, M. (2025). A Digital Twin of Dapagliflozin Pharmacokinetics and Pharmacodynamics in Type 2 Diabetes Mellitus: Modeling Variability in Dosing, Hepatorenal Impairment, and Food Effects. https://doi.org/10.20944/preprints202511.0981.v1
    20. Ricken, T., Azhdari, M., Rezazadeh, G., Pathak, R., & Seyedpour, S. M. (2025). Heat Transfer Modeling in Two-Dimensional Porous Composite Structure with Polymer Matrix and Metal Particles Using the Virtual Element Method Under Laser Heating. In W. Graf, R. Fleischhauer, J. Storm, & I. Wollny (Eds.), Advances and Challenges in Computational Mechanics (pp. 403–417). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-93213-7_32

  3. 2024

    1. Tautenhahn, H.-M., Ricken, T., Dahmen, U., Mandl, L., Bütow, L., Gerhäusser, S., Lambers, L., Chen, X., Lehmann, E., Dirsch, O., & König, M. (2024). SimLivA–Modeling ischemia‐reperfusion injury in the liver: A first step towards a clinical decision support tool. GAMM-Mitteilungen. https://doi.org/10.1002/gamm.202370003
    2. Suditsch, M., Egli, F., Lambers, L., Wagner, A., & Ricken, T. (2024). biphasic-kinematic-growth. https://doi.org/10.18419/darus-4361
    3. Seyedpour, S. M., Azhdari, M., Lambers, L., Ricken, T., & Rezazadeh, G. (2024). One-dimensional thermomechanical bio-heating analysis of viscoelastic tissue to laser radiation shapes. International Journal of Heat and Mass Transfer, 218, 124747. https://doi.org/10.1016/j.ijheatmasstransfer.2023.124747
    4. Brodbeck, M., Suditsch, M., Seyedpour, S. M., & Ricken, T. (2024). Data for: Phase transition in porous materials - Effects of material parameters and deformation regime on mass conservativity. DaRUS. https://doi.org/10.18419/darus-4460
    5. Golebiewski, M., Bader, G., Gleeson, P., Gorochowski, T. E., Keating, S. M., König, M., Myers, C. J., Nickerson, D. P., Sommer, B., Waltemath, D., & Schreiber, F. (2024). Specifications of standards in systems and synthetic biology: status, developments, and tools in 2024. Journal of Integrative Bioinformatics. https://doi.org/10.1515/jib-2024-0015
    6. Smith, L. P., Bergmann, F. T., Garny, A., Helikar, T., Karr, J., Nickerson, D., Sauro, H., Waltemath, D., & König, M. (2024). The simulation experiment description markup language (SED-ML): language specification for level 1 version 5. Journal of Integrative Bioinformatics. https://doi.org/10.1515/jib-2024-0008
    7. Azhdari, M., Rezazadeh, G., Lambers, L., Ricken, T., Tautenhahn, H.-M., Tautenhahn, F., & Seyedpour, S. M. (2024). Refining thermal therapy: Temperature distribution modeling with distinct absorption in multi-layered skin tissue during infrared laser exposure. International Communications in Heat and Mass Transfer, 157, 107818. https://doi.org/10.1016/j.icheatmasstransfer.2024.107818
    8. Brodbeck, M., Suditsch, M., Seyedpour, S. M., & Ricken, T. (2024). Phase transition in porous materials: effects of material parameters and deformation regime on mass conservativity. Computational Mechanics. https://doi.org/10.1007/s00466-024-02557-2
    9. Gerhäusser, S., Lambers, L., Mandl, L., Franquinet, J., Ricken, T., & König, M. (2024). Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury. https://doi.org/10.1101/2024.03.26.586870
    10. Tautenhahn, H.-M., Ricken, T., Dahmen, U., Mandl, L., Bütow, L., Gerhäusser, S., Lambers, L., Chen, X., Lehmann, E., Dirsch, O., & König, M. (2024). SimLivA-Modeling ischemia-reperfusion injury in the liver: A first step towards a clinical decision support tool. GAMM-Mitteilungen. https://doi.org/10.1002/gamm.202370003
    11. Trivedi, Z., Wychowaniec, J. K., Gehweiler, D., Sprecher, C. M., Boger, A., Gueorguiev, B., D’Este, M., Ricken, T., & Röhrle, O. (2024). Rheological Analysis and Evaluation of Measurement Techniques for Curing Poly(Methyl Methacrylate) Bone Cement in Vertebroplasty. ACS Biomaterials Science & Engineering, 10, Article 7. https://doi.org/10.1021/acsbiomaterials.4c00417
    12. Suditsch, M., Wagner, A., & Ricken, T. (2024). OncoTUM models. DaRUS. https://doi.org/10.18419/DARUS-4647
    13. Raman, K., Kratochvíl, M., Olivier, B. G., König, M., Sengupta, P., Baskaran, D. K. K., Nguyen, T. V. N., Lobo, D., Wilken, S. E., Tiwari, K. K., Raghu, A. K., Palanikumar, I., Raajaraam, L., Ibrahim, M., Balakrishnan, S., Umale, S., Bergmann, F., Malpani, T., Satagopam, V. P., et al. (2024). FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models. https://doi.org/10.1101/2024.09.24.614797

  4. 2023

    1. Lambers, L., Waschinsky, N., Schleicher, J., König, M., Tautenhahn, H.-M., Albadry, M., Dahmen, U., & Ricken, T. (2023). Quantifying Fat Zonation in Liver Lobules: An IntegratedMultiscale In-silico Model Combining DisturbedMicroperfusion and Fat Metabolism via aContinuum-Biomechanical Bi-scale, Tri-phasic Approach. https://doi.org/10.21203/rs.3.rs-3348101/v1
    2. Seyedpour, S. M., Lambers, L., Rezazadeh, G., & Ricken, T. (2023). Mathematical modelling of the dynamic response of an implantable enhanced capacitive glaucoma pressure sensor. Measurement: Sensors, 100936. https://doi.org/10.1016/j.measen.2023.100936
    3. Maheshvare, M. D., Raha, S., König, M., & Pal, D. (2023). A Consensus Model of Glucose-Stimulated Insulin Secretion in the Pancreaticβ-Cell. https://doi.org/10.1101/2023.03.10.532028
    4. König, M., Gleeson, P., Golebiewski, M., Gorochowski, T. E., Hucka, M., Keating, S. M., Myers, C. J., Nickerson, D. P., Sommer, B., Waltemath, D., & Schreiber, F. (2023). Specifications of standards in systems and synthetic biology: status and developments in 2022 and the COMBINE meeting 2022. Journal of Integrative Bioinformatics. https://doi.org/10.1515/jib-2023-0004
    5. Welsh, C., Xu, J., Smith, L., König, M., Choi, K., & Sauro, H. M. (2023). libRoadRunner 2.0: a high performance SBML simulation and analysis library. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac770
    6. Küttner, J., Grzegorzewski, J., Tautenhahn, H.-M., & König, M. (2023). A physiologically based pharmacokinetic model for CYP2E1 phenotyping via chlorzoxazone. https://doi.org/10.1101/2023.04.12.536571

Head of Research Group

Researchers

This image showsMarlon Suditsch

Marlon Suditsch

M.Sc.

Head of Computational Biomechanics Group, Research Assistant

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