Arrays of elastic pillars are used in biophysical experiments as sensors for traction forces. The evaluation of the forces can be complicated if they are coupled to the pillar displacements over large distances. This is the case if many of the pillars are interconnected by elastic linkages as, for example, in fiber networks that are grown on top of pillars. To calculate the traction forces in such a network, we developed a set of nonlinear inhomogeneous equations relating the forces in the linking elements to the resulting pillar deflections. We chose a homogeneous, activated two-dimensional network of cytoskeletal actin filaments to illustrate that a pillar substrate is generally not a force sensor but a force-gradient sensor. In homogeneous networks the forces acting along the filaments can be approximated by analyzing only pillar deflections in the edge zones of the substrate and by integration over the corresponding force gradients.
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http://dx.doi.org/10.1002/cphc.200500109 | DOI Listing |
BMC Mol Cell Biol
January 2025
Institute of Future Biophysics, Institutskiy per. 9, Dolgoprudny, Moscow Oblast, Moscow, Russia.
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View Article and Find Full Text PDFPerception
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State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, China; University of Chinese Academy of Sciences, China; Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, China.
Previous research has indicated that exposure to sensory stimuli of short or long durations influences the perceived duration of subsequent stimuli within the same modality. However, it remains unclear whether this adaptation is driven by the stimulus physical duration or by the perceived duration. We hypothesized that the absence of cross-modal duration adaptation observed in earlier studies was due to the mismatched perceived durations of adapting stimuli.
View Article and Find Full Text PDFActa Pharmacol Sin
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Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantially. This progress inspired the development of the first genome-wide small molecule targets scanning method.
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