SimTech PN2-2

Data- and Model-Driven Multiscale Simulation of Tumor Growth in Liver Cell, Tissue and Organ

This project is part of the Project Network 2: In Silico Models of Coupled Biological Systems of the Cluster of Excellence "Data-Integrated Simulation Science (SimTech)" and thus funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2075 – 390740016.  

The project aims at a better understanding of the development of tumors in the liver, which is necessary to predict the process of cancer growth and retardation in the liver. The scope is as follows:

  1. Develop a deterministic multiscale model for tumor growth and metastases,
  2. improve the efficiency in the numerical calculation,
  3. integrate data obtained experimentally and in silico, and
  4. develop and apply polymorphic uncertainty quantification (UQ) procedures.

Further information: https://www.simtech.uni-stuttgart.de/exc/research/pn/pn2/pn2-2a/

Publications

  1. 2026

    1. 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
    2. Mandl, L., Nayak, D., Ricken, T., & Goswami, S. (2026). Physics-informed time-integrated DeepONet: Temporal tangent space operator learning for high-accuracy inference. Computer Methods in Applied Mechanics and Engineering, 455, 118917. https://doi.org/10.1016/j.cma.2026.118917
    3. Pathak, R., Seyedpour, S. M., Kutschan, B., Thom, A., Thoms, S., & Ricken, T. (2026). Computational modeling of sea ice freezing dynamics across scales. International Journal of Mechanical Sciences, 309, 111010. https://doi.org/10.1016/j.ijmecsci.2025.111010
    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

  2. 2025

    1. Suditsch, M., Egli, F. S., Lambers, L., & Ricken, T. (2025). Growth in biphasic tissue. International Journal of Engineering Science, 208, 104183. https://doi.org/10.1016/j.ijengsci.2024.104183
    2. 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. https://doi.org/10.1016/j.cma.2024.117586
    3. 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
    4. 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. Arasteh-Khoshbin, O., Seyedpour, S. M., Mandl, L., Lambers, L., & Ricken, T. (2024). Comparing durability and compressive strength predictions of hyperoptimized random forests and artificial neural networks on a small dataset of concrete containing nano SiO2 and RHA. European Journal of Environmental and Civil Engineering, 1–20. https://doi.org/10.1080/19648189.2024.2393881
    2. 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
    3. 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
    4. 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
    5. 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
    6. Lambers, L., Waschinsky, N., Schleicher, J., König, M., Tautenhahn, H.-M., Albadry, M., Dahmen, U., & Ricken, T. (2024). Quantifying fat zonation in liver lobules : an integrated multiscale in silico model combining disturbed microperfusion and fat metabolism via a continuum biomechanical bi-scale, tri-phasic approach. Biomechanics and modeling in mechanobiology, 23, Article 2. https://doi.org/10.1007/s10237-023-01797-0

  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. Suditsch, M., Ricken, T., & Wagner, A. (2023). Patient-specific simulation of brain tumour growth and regression. Pamm, 23, Article 1. https://doi.org/10.1002/pamm.202200213
    3. 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
    4. Azhdari, M., Seyedpour, S. M., Lambers, L., Tautenhahn, H.-M., Tautenhahn, F., Ricken, T., & Rezazadeh, G. (2023). Non-local three phase lag bio thermal modeling of skin tissue and experimental evaluation. International Communications in Heat and Mass Transfer, 149, 107146. https://doi.org/10.1016/j.icheatmasstransfer.2023.107146

  5. 2022

    1. Armiti-Juber, A., & Ricken, T. (2022). Model order reduction for deformable porous materials in thin domains via asymptotic analysis. Archive of Applied Mechanics, 92, Article 2. https://doi.org/10.1007/s00419-021-01907-3
    2. Bertrand, F., Brodbeck, M., & Ricken, T. (2022). On robust discretization methods for poroelastic problems: Numerical examples and counter-examples. Examples and Counterexamples, 2, 100087. https://doi.org/10.1016/j.exco.2022.100087
    3. Ricken, T., Schröder, J., Bluhm, J., Maike, S., & Bartel, F. (2022). Theoretical formulation and computational aspects of a two-scale homogenization scheme combining the TPM and FE 2 method for poro-elastic fluid-saturated porous media. International Journal of Solids and Structures, 241, 111412. https://doi.org/10.1016/j.ijsolstr.2021.111412

  6. 2021

    1. Christ, B., Collatz, M., Dahmen, U., Herrmann, K.-H., Höpfl, S., König, M., Lambers, L., Marz, M., Meyer, D., Radde, N., Reichenbach, J. R., Ricken, T., & Tautenhahn, H.-M. (2021). 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, 12. https://doi.org/10.3389/fphys.2021.733868
    2. Seyedpour, S. M., Nabati, M., Lambers, L., Nafisi, S., Tautenhahn, H.-M., Sack, I., Reichenbach, J. R., & Ricken, T. (2021). Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.733393
    3. Suditsch, M., Schröder, P., Lambers, L., Ricken, T., Ehlers, W., & Wagner, A. (2021). Modelling basal-cell carcinoma behaviour in avascular skin. Pamm, 20, Article 1. https://doi.org/10.1002/pamm.202000283
    4. Bertrand, F., Lambers, L., & Ricken, T. (2021). Least Squares Finite Element Method for Hepatic Sinusoidal Blood Flow. Pamm, 20, Article 1. https://doi.org/10.1002/pamm.202000306
    5. Lambers, L., Suditsch, M., Wagner, A., & Ricken, T. (2021). A Multiscale and Multiphase Model of Function-Perfusion Growth Processes in the Human Liver. Pamm, 20, Article 1. https://doi.org/10.1002/pamm.202000290
    6. Suditsch, M., Lambers, L., Ricken, T., & Wagner, A. (2021). Application of a continuum-mechanical tumour model to brain tissue. Pamm, 21, Article 1. https://doi.org/10.1002/pamm.202100204
    7. Seyedpour, S. M., Valizadeh, I., Kirmizakis, P., Doherty, R., & Ricken, T. (2021). Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method. Water, 13, Article 3. https://doi.org/10.3390/w13030383
    8. Lambers, L., Mielke, A., & Ricken, T. (2021). 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, 21, Article 1. https://doi.org/10.1002/pamm.202100190
    9. Armiti-Juber, A., & Ricken, T. (2021). Model order reduction for deformable porous materials in thin domains via asymptotic analysis. Archive of Applied Mechanics. https://doi.org/10.1007/s00419-021-01907-3

  7. 2019

    1. Lambers, L., Ricken, T., & König, M. (2019). Model Order Reduction (MOR) of Function--Perfusion--Growth Simulation in the Human Fatty Liver via Artificial Neural Network (ANN). Pamm, 19, Article 1. https://doi.org/10.1002/pamm.201900429
    2. Ricken, T., & Lambers, L. (2019). On computational approaches of liver lobule function and perfusion simulation. GAMM-Mitteilungen, 42, Article 4. https://doi.org/10.1002/gamm.201900016
This image showsNavina Waschinsky

Navina Waschinsky

Dr.-Ing.

Head of Optimization & Uncertainty Quantification Group, Researcher

This image showsTim Ricken

Tim Ricken

Univ.-Prof. Dr.-Ing.

Head of Department

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