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

PN2-2A

Project Description

Since hepatic cancer has a wide spread and is one of the leading causes of death worldwide, computational models become more important for the prediction of the disease and the development of personal treatment. Therefore, the aim of our project is to develop an efficient data-integrated multiscale and multiphase model for tumor growth in the liver. We want to include different size scales, namely the cell, tissue and organ scale, as well as different microscopic substances like treatment agents or nutrient concentrations. Furthermore, the model will integrate the development of fat during a non-alcoholic fatty liver model. The model is based on ordinary differential equations (ODE), describing the metabolic processes on the cell scale, as well as on partial differential equations (PDE) for the description of blood transport and elastic tissue behaviour of the liver lobule. The project aims to enhance this PDE-ODE approach by model order reduction (MOR) via an artificial neural network (ANN) for the cell scale to improve the computational performance of the model. We will train the ANN on experimental and in silico data generated by our project partners so that we get a PDE-ANN approach. To improve the robustness and efficiency of the model, we will develop polymorphic uncertainty quantification procedures via a combination of the variational sensitivity analysis and the Bayesian approach.

Project Number PN 2-2A
Project Name Data and Model-Driven Multiscale Simulation of Tumor Growth in Liver Cell, Tissue and Organ
Project Duration June 2020 - May 2023
Project Leader Tim Ricken
Project Members Arndt Wagner, Collaborative Applicant
Lena Lambers, doctoral researcher
Marlon Suditsch
Project Partners Arndt Wagner (PN 2-2B)
Nicole Radde/ Markus Morrison (PN 2-1)
Felix Fritzen (PN 3-1)
Marc-André Keip (PN 3-5)

Publications of PN 2-2A

  1. 2023

    1. S. M. Seyedpour, A. Thom, and T. Ricken, “Simulation of Contaminant Transport through the Vadose Zone: A Continuum Mechanical Approach within the Framework of the Extended Theory of Porous Media (eTPM),” Water, vol. 15, no. 2, Art. no. 2, 2023, doi: 10.3390/w15020343.
    2. 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, no. 1, Art. no. 1, 2023, doi: 10.1038/s41598-023-42141-x.
    3. L. Lambers et al., “Quantifying Fat Zonation in Liver Lobules: An IntegratedMultiscale In-silico Model Combining DisturbedMicroperfusion and Fat Metabolism via aContinuum-Biomechanical Bi-scale, Tri-phasic Approach,” Sep. 2023, doi: 10.21203/rs.3.rs-3348101/v1.
    4. 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.
  2. 2022

    1. 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.
  3. 2021

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
  4. 2019

    1. 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.
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