ArticleBiophysical journal2013
Estimating the 3D pore size distribution of biopolymer networks from directionally biased data.
Article in Biophysical journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Extracellular matrix physical properties govern the diffusion of nanoparticles in tumor microenvironment.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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- 3D magnetically controlled spatiotemporal probing and actuation of collagen networks from a single cell perspective.Lab on a chip · 2021Article
- A computational framework for modeling cell-matrix interactions in soft biological tissues.Biomechanics and modeling in mechanobiology · 2021Article
- Cellular Pushing Forces during Mitosis Drive Mitotic Elongation in Collagen Gels.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Integrated Biophysical Characterization of Fibrillar Collagen-Based Hydrogels.ACS biomaterials science & engineering · 2020Article
- Deterministic actin waves as generators of cell polarization cues.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Computational-Based Design of Hydrogels with Predictable Mesh Properties.ACS biomaterials science & engineering · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pore size of biopolymer networks governs their mechanical properties and strongly impacts the behavior of embedded cells. Confocal reflection microscopy and second harmonic generation microscopy are widely used to image biopolymer networks; however, both techniques fail to resolve vertically oriented fibers. Here, we describe how such directionally biased data can be used to estimate the network pore size. We first determine the distribution of distances from random points in the fluid phase to the nearest fiber. This distribution follows a Rayleigh distribution, regardless of isotropy and data bias, and is fully described by a single parameter--the characteristic pore size of the network. The bias of the pore size estimate due to the missing fibers can be corrected by multiplication with the square root of the visible network fraction. We experimentally verify the validity of this approach by comparing our estimates with data obtained using confocal fluorescence microscopy, which represents the full structure of the network. As an important application, we investigate the pore size dependence of collagen and fibrin networks on protein concentration. We find that the pore size decreases with the square root of the concentration, consistent with a total fiber length that scales linearly with concentration.
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