Borges Augusto, Chara Osvaldo
Unit Sensory Biology and Organogenesis, Helmholtz Zentrum München, Munich, Germany.
Graduate School of Quantitative Biosciences, Ludwig Maximilian University, Munich, Germany.
Biochem Soc Trans. 2024 Dec 19;52(6):2579-2592. doi: 10.1042/BST20230225.
Cells exert forces on each other and their environment, shaping the tissue. The resulting mechanical stresses can be determined experimentally or estimated computationally using stress inference methods. Over the years, mechanical stress inference has become a non-invasive, low-cost computational method for estimating the relative intercellular stresses and intracellular pressures of tissues. This mini-review introduces and compares the static and dynamic modalities of stress inference, considering their advantages and limitations. To date, most software has focused on static inference, which requires only a single microscopy image as input. Although applicable in quasi-equilibrium states, this approach neglects the influence that cell rearrangements might have on the inference. In contrast, dynamic stress inference relies on a time series of microscopy images to estimate stresses and pressures. Here, we discuss both static and dynamic mechanical stress inference in terms of their physical, mathematical, and computational foundations and then outline what we believe are promising avenues for in silico inference of the mechanical states of tissues.
细胞相互之间以及与周围环境施加力，从而塑造组织。由此产生的机械应力可以通过实验确定，或使用应力推断方法进行计算估计。多年来，机械应力推断已成为一种用于估计组织相对细胞间应力和细胞内压力的非侵入性、低成本计算方法。本综述介绍并比较了应力推断的静态和动态模式，同时考虑了它们的优点和局限性。迄今为止，大多数软件都专注于静态推断，它只需要一张显微镜图像作为输入。虽然适用于准平衡状态，但这种方法忽略了细胞重排可能对推断产生的影响。相比之下，动态应力推断依赖于显微镜图像的时间序列来估计应力和压力。在这里，我们从物理、数学和计算基础方面讨论静态和动态机械应力推断，然后概述我们认为在计算机模拟推断组织机械状态方面有前景的途径。
