From Transcript to Tissue: Multiscale Modeling from Cell Signaling to Matrix Remodeling.
From Transcript to Tissue: Multiscale Modeling from Cell Signaling to Matrix Remodeling.
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DOI：
10.1007/s10439-020-02713-8
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发表时间：
2021-07
影响因子：
3.8
通讯作者：
Humphrey JD
中科院分区：
文献类型：
--
作者：
Irons L;Latorre M;Humphrey JD
组织水平的生物力学特性和功能来自于潜在的细胞信号传导，其调节质量沉积、组织和去除。在这里，我们耦合两个现有的建模框架，以捕捉相关的多尺度相互作用，一个血管水平的增长和重塑和细胞水平的信号，并通过模拟主动脉重塑说明实用程序。在血管水平上，我们采用了一个约束的混合物模型，描述周转的个别壁成分（弹性蛋白，壁内细胞和胶原蛋白），这已被证明是有用的预测不同的适应以及疾病的进展，使用现象学的本构关系。然而，我们现在寻求一个更好的机制理解这些过程，我们取代现象学的关系，在混合物模型与逻辑为基础的信号模型，这产生了一个系统的常微分方程预测胶原蛋白合成的变化，基质金属蛋白酶，细胞增殖反应改变壁内应力，壁切应力，外源性血管紧张素II。这种耦合的方法有望提高对细胞信号传导在实现组织稳态中的作用的理解，并允许我们对血管力学和细胞信号传导之间的反馈进行建模。我们验证我们的模型预测对高血压小鼠肾下腹主动脉的数据，以及从验证的现象学模型的结果，并考虑噪声信号和异质细胞群的影响。
Tissue-level biomechanical properties and function derive from underlying cell signaling, which regulates mass deposition, organization, and removal. Here, we couple two existing modeling frameworks to capture associated multiscale interactions—one for vessel-level growth and remodeling and one for cell-level signaling—and illustrate utility by simulating aortic remodeling. At the vessel level, we employ a constrained mixture model describing turnover of individual wall constituents (elastin, intramural cells, and collagen), which has proven useful in predicting diverse adaptations as well as disease progression using phenomenological constitutive relations. Nevertheless, we now seek an improved mechanistic understanding of these processes; we replace phenomenological relations in the mixture model with a logic-based signaling model, which yields a system of ordinary differential equations predicting changes in collagen synthesis, matrix metalloproteinases, and cell proliferation in response to altered intramural stress, wall shear stress, and exogenous angiotensin II. This coupled approach promises improved understanding of the role of cell signaling in achieving tissue homeostasis and allows us to model feedback between vessel mechanics and cell signaling. We verify our model predictions against data from the hypertensive murine infrarenal abdominal aorta as well as results from validated phenomenological models, and consider effects of noisy signaling and heterogeneous cell populations.
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影响因子：
2.4
作者：
Aparicio, Pedro;Thompson, Mark S.;Watton, Paul N.
通讯作者：
Watton, Paul N.
影响因子：
4.3
作者：
Irons, Linda;Humphrey, Jay D.
通讯作者：
Humphrey, Jay D.
影响因子：
--
作者：
Kraeutler, Matthew J.;Soltis, Anthony R.;Saucerman, Jeffrey J.
通讯作者：
Saucerman, Jeffrey J.
影响因子：
3.5
作者：
Estrada AC;Yoshida K;Saucerman JJ;Holmes JW
通讯作者：
Holmes JW
DOI：
10.1161/hypertensionaha.115.06262
发表时间：
2016-05
期刊：
Hypertension (Dallas, Tex. : 1979)
影响因子：
--
作者：
Bersi MR;Bellini C;Wu J;Montaniel KRC;Harrison DG;Humphrey JD
通讯作者：
Humphrey JD