LaBelle Steven A, Sadrabadi Mohammadreza Soltany, Baek Seungik, Mofrad Mohammad R K, Weiss Jeffrey A, Arzani Amirhossein
Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112.
University of Utah.
J Biomech Eng. 2025 Jun 1;147(6). doi: 10.1115/1.4068290.
Multiscale coupling between cell-scale biology and tissue-scale mechanics is a promising approach for modeling disease growth. In such models, tissue-level growth and remodeling (G&R) are driven by cell-level signaling pathways and systems biology models, where each model operates at different scales. Herein, we generate multiscale G&R models to capture the associated multiscale connections. At the cell-scale, we consider systems biology models in the form of systems of ordinary differential equations (ODEs) and partial differential equations (PDEs) representing the reactions between the biochemicals causing the growth based on mass-action or logic-based Hill-type kinetics. At the tissue-scale, we employ kinematic growth in continuum frameworks. Two illustrative test problems (a tissue graft and aneurysm growth) are examined with various chemical signaling networks, boundary conditions, and mechano-chemical coupling strategies. We extend two open-source software frameworks-febio and fenics-to disseminate examples of multiscale growth and remodeling simulations. One-way and two-way coupling between the systems biology and the growth models are compared and the effect of biochemical diffusivity and ODE versus PDE-based systems biology modeling on the G&R results are studied. The results show that growth patterns emerge from reactions between biochemicals, the choice between ODEs and PDEs systems biology modeling, and the coupling strategy. Cross-verification confirms that results for febio and fenics are nearly identical. We hope that these open-source tools will support reproducibility and education within the biomechanics community.
细胞尺度生物学与组织尺度力学之间的多尺度耦合是一种很有前景的疾病生长建模方法。在这类模型中，组织水平的生长与重塑（G&R）由细胞水平的信号通路和系统生物学模型驱动，其中每个模型在不同尺度上运行。在此，我们生成多尺度G&R模型以捕捉相关的多尺度联系。在细胞尺度，我们考虑以常微分方程（ODE）和偏微分方程（PDE）系统形式表示的系统生物学模型，这些方程基于质量作用或基于逻辑的希尔型动力学描述导致生长的生物化学物质之间的反应。在组织尺度，我们在连续介质框架中采用运动学生长。通过各种化学信号网络、边界条件和机械化学耦合策略研究了两个示例测试问题（组织移植和动脉瘤生长）。我们扩展了两个开源软件框架——febio和fenics——以传播多尺度生长和重塑模拟的示例。比较了系统生物学与生长模型之间的单向和双向耦合，并研究了生物化学扩散率以及基于ODE与基于PDE的系统生物学建模对G&R结果的影响。结果表明，生长模式源于生物化学物质之间的反应、ODE和PDE系统生物学建模之间的选择以及耦合策略。交叉验证证实febio和fenics的结果几乎相同。我们希望这些开源工具将支持生物力学领域内的可重复性研究和教育。
