390 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 study reveals that while RNA i-motifs are globally unfolded at neutral pH in ensemble experiments, single-molecule techniques detect a small but potentially biologically significant population of folded structures, suggesting their possible relevance in cellular environments.
This paper introduces a reproducible computational synthetic inner mitochondrial membrane (syn-IMM) framework that reveals how cardiolipin composition and membrane leak interact to control ATP output, identifying a narrow optimal cardiolipin window and establishing quantitative design rules for programmable bioenergetic membranes.
This study critically evaluates the Therapeutics Data Commons (TDC) ADMET leaderboards and reveals that most top-ranked models suffer from irreproducibility, data leakage, or test-set overfitting, with only three methods passing rigorous verification, thereby highlighting the urgent need for stricter benchmarking standards including hidden test sets and standardized submission environments.
This study presents the first systematic evaluation of six image representation pipelines for deep learning-based single-cell classification in holographic imaging flow cytometry, revealing a clear trade-off between computational efficiency and accuracy while providing a Pareto-optimal framework to guide pipeline selection for biomedical applications.
The authors present QuantiTrack, a user-friendly MATLAB-based software that provides an end-to-end solution for single-molecule tracking analysis in living cells, demonstrating its utility by revealing how hormone washout alters the binding dynamics and activation states of the glucocorticoid receptor.
By combining droplet-based microfluidic mixing with single-molecule FRET, this study reveals that poly(ADP-ribose) (PAR) triggers length-dependent nucleosome decompaction through electrostatic competition with DNA for histone binding, where only polymers exceeding ten ADP-ribose units efficiently drive rapid chromatin opening.
This paper presents a Dynamical Graph Grammar framework to model the actin cytoskeleton and membrane dynamics within dendritic spine heads, demonstrating how specific actin-binding proteins and their interactions govern synaptic morphodynamics through biophysically constrained stochastic simulations.
This paper presents a novel, fully automated, and self-consistent iterative optimization method that accurately retrieves the unknown rotation angles of single flowing cells, thereby significantly enhancing the reliability and scalability of Holo-Tomographic Flow Cytometry for label-free 3D refractive index tomography.
This paper presents a comprehensive theoretical framework for simulating dynamic NMR lineshapes in lipid bicelles and nanodiscs that explicitly accounts for coupled lipid diffusion, orientational distributions, and membrane thinning, thereby enabling the quantitative interpretation of anisotropic interactions and membrane structural changes induced by biomolecular association.
This paper introduces MiCSPARC, a user-friendly cryo-EM processing pipeline built on CryoSPARC that utilizes automated particle picking and rapid 3D refinement to routinely generate high-resolution, seam-corrected structures of both decorated and undecorated microtubules.