Hindrances to precise recovery of cellular forces in fibrous biopolymer networks
نویسندگان
چکیده
منابع مشابه
Reconstruction of cellular forces in fibrous biopolymer network
How cells move through 3d extracellular matrix (ECM) is of increasing interest in attempts to understand important biological processes such as cancer metastasis. Just as in motion on 2d surfaces, it is expected that experimental measurements of cell-generated forces will provide valuable information for uncovering the mechanisms of cell migration. Here, we use a lattice-based mechanical model ...
متن کاملBiopolymer Networks and Cellular Mechanosensing
Cells and tissues are mechanical as well as biochemical machines, and cellular response to mechanical cues can have as large an influence on structure and function as chemical signals. The mechanical properties of cells are largely determined by networks of semiflexible polymers forming the cytoskeleton, which has viscoelastic properties that differ in important ways from the viscoelasticity of...
متن کاملCellular forces and matrix assembly coordinate fibrous tissue repair.
Planar in vitro models have been invaluable tools to identify the mechanical basis of wound closure. Although these models may recapitulate closure dynamics of epithelial cell sheets, they fail to capture how a wounded fibrous tissue rebuilds its 3D architecture. Here we develop a 3D biomimetic model for soft tissue repair and demonstrate that fibroblasts ensconced in a collagen matrix rapidly ...
متن کاملTheory of Biopolymer Stretching at High Forces.
We provide a unified theory for the high force entropic elasticity of biopolymers solely in terms of the persistence length, ξp , and the monomer spacing, a. When the force f>ℱ h ~ kBTξp /a2 the biopolymers behave as freely jointed chains (FJCs) while in the range ℱ l ~ kBT/ξp <f<ℱ h the worm-like chain (WLC) is a better model. We show that ξp can be estimated from the force extension curve (FE...
متن کاملPrecise Recovery of Latent Vectors from Generative Adversarial Networks
Generative adversarial networks (GANs) transform latent vectors into visually plausible images. It is generally thought that the original GAN formulation gives no out-of-the-box method to reverse the mapping, projecting images back into latent space. We introduce a simple, gradient-based technique called stochastic clipping. In experiments, for images generated by the GAN, we precisely recover ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Biology
سال: 2018
ISSN: 1478-3975
DOI: 10.1088/1478-3975/aaa107