In this contribution we investigate in mathematical modeling and efficient simulation of biological cells with a particular emphasis on effective modeling of structural properties that originate from active forces generated from polymerization and depolymerization of cytoskeletal components. In detail, we propose a nonlinear continuum approach to model microtubule-based forces which have recently been established as central components of cell mechanics during early fruit fly wing development. The model is discretized in space using the finite-element method. Although the individual equations are decoupled by a semi-implicit time discretization, the discrete model is still computationally demanding. In addition, the parameters needed for the effective model equations are not easily available and have to be estimated or determined by repeatedly solving the model and fitting the results to measurements. This drastically increases the computational cost. Reduced basis methods have been used successfully to speed up such repeated solves, often by several orders of magnitude. However, for the complex nonlinear models regarded here, the application of these model order reduction methods is not always straight-forward and comes with its own set of challenges. In particular, subspace construction using the Proper Orthogonal Decomposition (POD) becomes prohibitively expensive for reasonably fine grids. We thus propose to combine the Hierarchical Approximate POD, which is a general, easy-to-implement approach to compute an approximate POD, with an Empirical Interpolation Method to efficiently generate a fast to evaluate reduced order model. Numerical experiments are given to demonstrate the applicability and efficiency of the proposed modeling and simulation approach.
翻译:在此贡献中,我们调查生物细胞的数学建模和高效模拟,特别强调对细胞骨骼组件聚合和脱聚合产生的活性力量产生的结构特性的有效建模。我们详细建议对模型微囊型力量采取非线性连续方法,这些力量最近在早期果蝇翅膀开发过程中作为细胞机械的核心组成部分而建立,在空间中采用有限度方法分离。虽然个别方程式通过半模糊时间分解而分解,但离散式模型在计算上仍然很困难。此外,有效模型方程式的实用性所需的参数不易获得,并且必须通过反复解决模型和将结果与测量相匹配来估计或确定。这大大增加了计算成本。基础方法已被成功地用于加速这种重复的解决方案,通常使用若干级的大小。然而,对于此处所看到的复杂的非线性模型模型,应用这些减少顺序的方法并不总是直截然相反的,而且随着其自身的挑战组合而出现。 特别是,低空间下级模型所需的参数要通过反复的模型来估算模型的精确度,从而将一个稳定的直径直的直径直的直径直的直径直径直的直径直的轨道。