388 papers
Biophysics sits at the fascinating intersection where the laws of physics meet the complexity of living systems. This field uses tools like light, electricity, and mechanical forces to decode how cells move, how proteins fold, and how our senses translate the world around us. Rather than just observing biology, biophysicists measure and model life to understand the fundamental machinery that powers every organism.
On Gist.Science, we make these discoveries accessible by curating the latest preprints directly from bioRxiv. Our team processes every new submission in this category, providing both clear, plain-language overviews and detailed technical summaries so readers of all backgrounds can grasp the cutting-edge science. Below are the most recent biophysics papers from bioRxiv, ready for you to explore.
This paper introduces a physics-based inversion framework that converts nanoscale dynamic AFM measurements of living plant cell walls into spatially resolved mechanical fields, revealing how local relaxation times are encoded in the coupling between storage and dissipation to bridge molecular activity with continuum growth laws.
This study reveals that bacterial cell volume growth exhibits subdiffusive dynamics driven by the viscoelastic mechanical properties of the peptidoglycan cell wall rather than biological regulatory programs, challenging standard white-noise growth models.
Using vertex models and zebrafish mesendoderm data, this study demonstrates that optimal collective migration in heterogeneous tissues occurs at an intermediate adhesion strength that balances cluster cohesion with the interfacial remodeling necessary for forward motion.
This systematic review and meta-analysis reinterprets long-term divergence exponents in gait analysis not as a measure of stability, but as the "Attractor Complexity Index," a biomarker reflecting gait complexity and automaticity that is sensitive to attentional demands and motor-cognitive control reorganization.
This paper presents an extension of the GPU-accelerated OpenCafeMol simulator to support 3SPN.2 DNA models and enhanced DNA-protein interactions, achieving significant speed-ups that enable long-timescale simulations of complex biological processes like SMC-mediated DNA loop extrusion.
ChironRNA is an E(3)-equivariant diffusion model that employs a hierarchical all-atom and coarse-grained approach to effectively resolve steric clashes and reconstruct missing atoms in RNA structures, significantly outperforming traditional refinement methods.
This study combines an in silico model and in vivo experiments to demonstrate that heterotypic adhesion between migrating cells and epithelial substrates tunes transepithelial migration efficiency in a non-monotonic manner, identifying an optimal adhesion regime that accelerates cell exit.
This study presents a large-scale prospective evaluation demonstrating that while deep learning co-folding methods like AlphaFold3, Boltz-2, and Chai-1 can accurately predict ligand-bound protein structures and show some correlation with experimental potency, they fail to recapitulate key conformational changes, suggesting that integrating these deep learning approaches with traditional physics-based docking scores offers the most promising strategy for improving hit prioritization in drug discovery.
This paper presents a filament-resolved computational model demonstrating how the interplay between Arp2/3-mediated branching and fascin-mediated bundling drives the self-organization of distinct actin architectures (lamellipodia, filopodia, and reticulated networks) and their subsequent coupling to membrane deformation to regulate cell shape.
The paper introduces MorphCurveVAE, a two-stage deep learning framework that utilizes a multi-branch variational auto-encoder and topologically-aware principal curves to reconstruct continuous, biologically plausible 3D morphological trajectories from static microscopy snapshots, effectively modeling dynamic cellular processes like mitosis without requiring time-resolved data.