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In this work we show how a network inspired by a coarse-grained description of actomyosin cytoskeleton can learn - in a contrastive learning framework - from environmental perturbations if it is endowed with mechanosensitive proteins and motors. Our work is a proof of principle for how force-sensitive proteins and molecular motors can form the basis of a general strategy to learn in biological systems. Our work identifies a minimal biologically plausible learning mechanism and also explores its implications for commonly occuring phenomenolgy such as adaptation and homeostatis.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12045384 | PMC |
Key cellular processes rely on the transduction of extracellular mechanical signals by specialized membrane receptors, including adhesion G-protein-coupled receptors (aGPCRs). While recent studies support aGPCR activation via shedding of the extracellular GAIN domain, shedding-independent signaling mechanisms have also been observed. However, the molecular basis underlying these distinct activation modes remains poorly understood.
View Article and Find Full Text PDFNPJ Biol Phys Mech
August 2025
Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA.
Alterations of the extracellular matrix (ECM), including both mechanical (such as stiffening of the ECM) and chemical (such as variation of adhesion proteins and deposition of hyaluronic acid (HA)) changes, in malignant tissues have been shown to mediate tumor progression. To survey how cells from different tissue types respond to various changes in ECM mechanics and composition, we measured physical characteristics (adherent area, shape, cell stiffness, and cell speed) of 25 cancer and 5 non-tumorigenic cell lines on 7 different substrate conditions. Our results indicate substantial heterogeneity in how cell mechanics changes within and across tissue types in response to mechanosensitive and chemosensitive changes in ECM.
View Article and Find Full Text PDFAdv Healthc Mater
July 2025
Department of Biomedical Engineering, Department of Medicine, Columbia University, New York, 10027, USA.
Variability in T cell performance presents a major challenge to adoptive cellular immunotherapy (ACT). This includes expansion of a small starting population into therapeutically effective numbers, which can fail due to differences between individuals and disease states. Intriguingly, modulating the mechanical stiffness of materials used to activate T cells can rescue subsequent expansion.
View Article and Find Full Text PDFCancers (Basel)
May 2025
Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA.
Long-range intercellular communication is essential for multicellular biological systems to regulate multiscale cell-cell interactions and maintain life. Growing evidence suggests that intercellular calcium waves (ICWs) act as a class of long-range signals that influence a broad spectrum of cellular functions and behaviors. Importantly, mechanical signals, ranging from single-molecule-scale to tissue-scale in vivo, can initiate and modulate ICWs in addition to relatively well-appreciated biochemical and bioelectrical signals.
View Article and Find Full Text PDFJ Chem Theory Comput
May 2025
Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Proteins are inherently dynamic molecules, and their conformational transitions among various states are essential for numerous biological processes, which are often modulated by their interactions with surrounding environments. Although molecular dynamics (MD) simulations are widely used to investigate these transitions, all-atom (AA) methods are often limited by short time scales and high computational costs, and coarse-grained (CG) implicit-solvent Go̅-like models are usually incapable of studying the interactions between proteins and their environments. Here, we present an approach called Multiple-basin Go̅-Martini, which combines the recent Go̅-Martini model with an exponential mixing scheme to facilitate the simulation of spontaneous protein conformational transitions in explicit environments.
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