GAMM2025

Flexible macro-micro coupling for liver applications

8 Apr 2025, 15:00

20m

Room 9

PP04: 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

Speakers

Steffen Gerhäusser Benjamin Uekermann

Description

Simulation-based, patient-specific risk assessment via a digital liver twin has enormous potential in clinical applications such as personalized drug dosing or evaluation of the status and impairment of liver grafts before transplantation [1]. We present a flexible software framework for coupling tissue-scale and cellular-scale processes using FEniCS [2], libRoadRunner [3], and preCICE [4]. Tissue-scale phenomena like deformation and fluid transport are modeled with partial differential equations (PDEs) within the framework of the Theory of Porous Media (TPM). These advection-diffusion-reaction systems are extended by cellular-scale reaction terms to model hepatocyte functions. Efficient and robust coupling between the tissue-scale solver FEniCS (PDE) and the cellular-scale solver libRoadRunner (ODE) is facilitated through the preCICE coupling library. A Python-based package, the Micro Manager [5], controls all the micro-simulations and is itself coupled via preCICE. This segregation of the managing software from the coupling library enables adaptive and parallel execution of cellular models. This modular framework supports the flexible exchange of metabolic models, increasing potential use-cases to various clinical scenarios. The numerical study investigates different coupling schemes (explicit/implicit) and their interplay to different boundary value problems (BVP). To demonstrate the capabilities of such digital lobular structures, we showcase the coupled framework with different micro models, e.g. ischemic-reperfusion-injury (IRI) or substrate-product-toxin (SPT) metabolism. The outcome of this approach paves the way for further methods of data integration or surrogate modeling on the roadmap towards the digital liver twin.

[1] Tautenhahn H. et al. GAMM-Mitteilungen. (2024)47
[2] Baratta I. A. et al (2023). Zenodo. doi: 10.5281/zenodo.10447666.
[3] Welsh C. et al., Bioinformatics (2023)39
[4] Chourdakis G. et al. Open Research (2022)2
[5] Desai I. et al. Journal of Open Source Software (2023) [6] Gerhäusser S. et al. bioRxiv doi: https://doi.org/10.1101/2024.03.26.586870