An eco-evolutionary framework including evolution in infectiousness and antigenic features reveals that mildly infectious viruses might be more likely to become endemic that those with higher infectiousness.

The study examines noise contributions from random cell division times, extending beyond the effects of gene expression and partitioning noise, and presents the resulting analytical cyclo-stationary distributions of mRNA and protein counts.

Scaling theories differ for spike response models of in vitro and in vivo circuits — representing different universality classes that both exhibit anomalous scaling at a critical balance of inhibition and excitation.

Networks of stochastically spiking neurons can be reduced to combinatorial threshold-linear networks, representing a new tool for analyzing neural networks with clustered connectivity.

Chemotactic cells working together balance sensing and signal modulation, showing how shared information speeds and stabilizes collective search for targets.

A graph-based generative model designed to capture long-range chromatin interaction patterns from Hi-C data reveals a latent structure of chromatin interactions in the GM12878 lymphoblastoid cell line that strongly align with known biological annotations.

Hierarchical cell identity emerges from the dynamic interplay between competition among enhancers for the recruitment of epigenetic readers and cooperation among enhancers in activating shared transcriptional programs.

Inferring division-induced force dipoles from tissue strain fields quantifies mechanical forces of cell division and their impact on tissue mechanics.

A mathematical framework reveals how morphogenesis mediates morphogen transport and compartmentalization, formalizes cell-cell interaction ranges in dynamic tissues, and provides new nondimensional numbers to assess when tissue deformation affects patterning.

Active persistent dynamics are shown to mimic some aspects of Hebbian unlearning — suggesting that nonequilibrium dynamics can provide an alternative way to improve associative recall in neural network models.

Combining concepts from stochastic dynamics, statistical physics and graph theory, a new theoretical study shows that large biochemical networks should have a quadratic Arrhenius plot, as seen in developmental processes.

Lagrangian coherent structures precede and drive long-term intercellular stress reorganization and are physically governed by the mechanical properties of intercellular junctions — revealing how cell motion can be used to infer biomechanical metrics.

Oriented tissue deformation (convergent extension) is unstable when signaling-gradient-controlled active stresses contract the tissue along the gradient direction, even for large diffusion, but it can be stable when active stresses extend the tissue in gradient direction.

Within viscoelastic 3D granular matrices, C. elegans transition from inefficient thrashing under low confinement to efficient crawling under high confinement - a response dictated by the worm’s physical interaction with its environment, without involving soft-touch mechanosensation.

Motility changes during starvation preserve nematic order but intensify defect-driven flows, promoting aggregation in Myxococcus xanthus.

By quantifying the information transmitted from receptor to ERK, the study shows that individual cells can resolve graded stimuli, transmitting over 2 bits of information even with brief, sparse inputs.

Negative effective viscosity in epithelial monolayers marks regions wherein cells expend more metabolic energy and move faster, hence quantifying the flow of energy in tissues.

While arginine-rich peptides accelerate the nucleation of inter-protein beta-sheet structures in TDP-43, RNA and HSP70 slow their emergence — revealing the importance of client biomolecules in the stability and aging kinetics of RNA binding proteins.

By combining ultrafast phase-contrast X-ray imaging and phase-contrast computed tomography imaging, this study details a way to simultaneously visualize nematode locomotion and obtain precise, whole-organism morphometric measurements.

This review article on quantum computing applications in genomics highlights its realistic potential for the field, theoretical limitations, and the conditions that a problem and an algorithm must satisfy to benefit from quantum speedup.