SimLivA

SIMulation-supported LIVer Assessment for donor organs (SimLivA) - Continuum-biomechanical modeling for staging of ischemia reperfusion injury during liver transplantation

As part of the Priority Programme SPP 2311:  Robust coupling of continuum-biomechanical in silico models to establish active biological system models for later use in clinical applications - Co-design of modeling, numerics and usability, SimLivA (Grant number 465194077) aims to mathematically model the impact of mechanical alterations due to steatosis and cold ischemia on early ischemia reperfusion injury in liver transplantation. Experimental and clinical data will be used to validate the coupled multiphase and multiscale PDE-ODE model of the liver lobule.

The project addresses the following research questions:

  1. How to co-design computational methods, experimental studies, clinical processes, and technical workflows?
  2. How to improve the multiscale continuum-biomechanical model for prediction of IRI?
  3. How to obtain experimental and clinical data that are essential to quantify the relationship between steatosis, ischemia and reperfusion injury?
  4. How to evaluate the clinical usability of the model?

For further information, click here.

Publications

  1. 2025

    1. Pathak, R., Seyedpour, S. M., Kutschan, B., Thoms, S., & Ricken, T. (2025). A coupled multiscale description of seasonal Physical--BioGeoChemical dynamics in Southern Ocean Marginal Ice Zone. Environmental Modelling & Software, 185, 106270. https://doi.org/10.1016/j.envsoft.2024.106270
    2. 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. 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. Pathak, R., Seyedpour, S. M., Kutschan, B., Thom, A., Thoms, S., & Ricken, T. (2024). Modeling freezing and BioGeoChemical processes in Antarctic sea ice. Pamm, 24, Article 2. https://doi.org/10.1002/pamm.202400047
    3. Albadry, M., Küttner, J., Grzegorzewski, J., Dirsch, O., Kindler, E., Klopfleisch, R., Liska, V., Moulisova, V., Nickel, S., Palek, R., Rosendorf, J., Saalfeld, S., Settmacher, U., Tautenhahn, H.-M., König, M., & Dahmen, U. (2024). Cross-species variability in lobular geometry and cytochrome P450 hepatic zonation: insights into CYP1A2, CYP2D6, CYP2E1 and CYP3A4. Frontiers in Pharmacology, 15. https://doi.org/10.3389/fphar.2024.1404938
    4. 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
    5. 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
    6. 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

  3. 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. 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
    3. Mandl, L., Mielke, A., Seyedpour, S. M., & Ricken, T. (2023). Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem. Scientific Reports, 13, Article 1. https://doi.org/10.1038/s41598-023-42141-x

  4. 2021

    1. 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
    2. 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
This image shows Luis Mandl

Luis Mandl

M.Sc.

Acting Head of Machine Learning Group, Research Assistant

This image shows Tim Ricken

Tim Ricken

Univ.-Prof. Dr.-Ing.

Head of Department

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