Biological Physics Papers of the Week

Using a hybrid functional form combining physics-based terms with a graph neural network, we present implicit-solvent, machine-learned CG models for POPC.

Here, we demonstrate that existing strategies based purely on features derived from an external electric field fail to distinguish certain environments, ultimately limiting their accuracy. As a solution, we propose an extension to the equivariant TensorNet architecture that mitigates this shortcoming by incorporating classical atoms close to the quantum region into the message-passing scheme, combined with features derived from the electric field for describing the environment farther away.

The biotin-streptavidin interaction is a key component of nanobiotechnology, yet the nature of its cooperativity remains a debated question. Here, we introduce a label-free spectroscopy approach based on confocal UV autofluorescence microscopy to probe the binding cooperativity of this high-affinity system.

Here we develop a general analytical framework based on a martingale-based orthogonal decomposition, applicable to arbitrarily complex upstream dynamics, including memory effects.

After a particle forms its first receptor--ligand bond near a cell membrane, stable attachment often requires a threshold number of additional bonds. This paper develops a biophysical kinetic theory of this post-capture stage.

Crowders generate an entropic depletion attraction, yet continuum models usually assume contact is already established or treat the approach deterministically. We formulate a thermodynamically consistent stochastic theory for this pre-contact stage.

To this end, this article investigates a Vicsek-Kuramoto model with distance-dependent higher-order interactions and obtains analytical results, albeit under certain assumptions, that characterize its collective dynamics.

We present Tessera, a distributed runtime that fixes workload shapes between rebuilds.

Coarse-grained models of protein conformational dynamics use $\mathrm{C}_\alpha$ geometry to describe the probabilities of regular helices, ordered turns and disordered conformations.

We investigate how fast neural fluctuations and slow synaptic variability asymmetrically influence the structural robustness of associative memories.

In the harmonic theory, decisions follow from a neural representation of direction, and a neighbour enters only as a stimulus at a bearing, the direction in which it lies. Here we show that a neighbour's spatial extent allows its orientation to enter through projection geometry alone, and the theory generates a matrix of coupling coefficients indexed by the neighbour's bearing and aspect.

Here we determine both the finite-size cutoff and the full crossover function connecting the branching regime to the large-event tail for an exchangeable absorbing event process motivated by Cauchy-stable recurrent dynamics.

Onsager's variational principle (OVP) constructs dissipative dynamics from free energy, dissipation, and input power, but deterministic rate laws alone do not identify fluctuations or chemical fuel consumption. We examine these distinctions for nonlinear reaction kinetics in active matter.

Here, we train a spring-network model of the bacterial cell wall and embed it in a motile agent.

This study reports the ex vivo dielectric properties of human adrenal glands, including the cortex, medulla, capsule, and tumours, based on the data obtained from four patients (diagnosed with Conn's syndrome, Cushing's syndrome, and Pheochromocytoma) who underwent unilateral adrenalectomy at the University Hospital Galway, Ireland.

This paper has developed the one-pole and two-pole Cole-Cole and Debye models for the dielectric properties of normal and diseased human trabecular bone tissue at microwave frequencies (0.5-8.5 GHz).

The cooperative gain they miss decays only as $1/\ell^2$, and their selfish adaptations leave the pair worse off than if both had kept their solitary strokes. Related swimmers, which also value the payoff of their neighbor, recover this gain, whereas competitors pay to hinder each other, both following a law that we derive analytically.

We focus on the classic model problem of the swimming of a flexible filament driven by preferred curvature forcing.

We consider a system that runs until it meets a constraint, such as a wall, and then resets.

And, can this be predicted from their geometry? We address these questions using a three-dimensional polycube model with programmable face-to-face binding, defining a structure's assembly rule probability as the chance that a randomly chosen rule set produces it.