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Bacterial flagellar motors switch rotation direction with high sensitivity to environmental inputs. A theoretical model explains how torque-dependent non-equilibrium switching contributes to ultrasensitivity.

Fur farming represents an important hub of cross-species transmission for viral zoonoses.

The Escherichia coli chemoreceptors form a cooperative array that controls the activity of histidine kinase CheA, which drives chemotaxis signaling. The authors propose a nonequilibrium lattice model showing that the observed asymmetric switching dynamics result from an interplay between dissipation within core units and cooperative coupling, enabling sensitive and rapid signaling responses.

Deciphering interactions between subunits in protein complexes is an important problem. By combining cryo-EM imaging and statistical modeling, Han and colleagues reveal a significant cooperativity between subunits in the clock protein hexamer KaiC.

A curious peak in the distribution describing stochastic switching in bacterial motility had researchers confounded. But a careful study performed under varying mechanical conditions has now revealed that the breaking of detailed balance is to blame.

Modelling the development of neurons is challenging because dendrite formation is highly stochastic. Here, the authors construct a mean-field model to describe the growth of class IV Drosophila sensory dendrites quantitatively.

Combining experiments with modeling, Wadhwa et al. propose a model for mechano-adaptation in the bacterial flagellar motor, finding that load-dependent transitions between multiple internal states govern the binding and unbinding of subunits.

Historically, most renormalization group studies have been performed for equilibrium systems. Here, I give a personal reflection on the unexpected outcome of studying non-equilibrium flocking using renormalization methods.

Bacterial chemoreceptors have multiple methylation sites, but whether the order of methylation matters is unclear. Here, the authors show that sequentially ordered methylation is critical for perfect adaptation and for attenuating the trade-off between accurate adaptation and high response gain.

A challenging problem in deep learning consists in developing theoretical frameworks suitable to study generalization. Feng and colleagues uncover a duality relation between neuron activities and weights in deep learning neural networks, and use it to show that sharpness of the loss landscape and norm of the solution act together in determining its generalization performance.

The energy cost for the synchronization of biochemical oscillators is determined under general conditions. This framework reveals a relationship between the KaiC ATPase activity and the synchronization of the KaiC hexamers.

It is well known that organisms profit from adapting to their environment. A study of stochastic adaptation dynamics shows that this comes at the expense of adaptive speed and accuracy—providing a framework for understanding adaptation in noisy biological systems.

Cells rely on coherent oscillatory processes, despite being subject to large fluctuations from their environment. Simple motifs found in all oscillatory systems are studied to determine the thermodynamic cost of maintaining this coherence.

Biochemical processes require both high sensitivity and low fluctuation which is incompatible with the fluctuation dissipation theorem. Here Fei et al. model biochemical oscillators to show how free energy dissipation leads to both a suppression of phase fluctuation and an enhancement of phase sensitivity.

Networks are useful ways to describe interactions between molecules in a cell, but predicting the real topology of large networks can be challenging. Here, the authors use deep learning to predict the topology of networks that perform biologically-plausible functions.

Complex systems, such as the Internet, are surprisingly resistant to random errors. But a new study warns against complacency — the feature that makes the Internet immune to accidents also makes it vulnerable to attack.

It is well established that odor-evoked excitation in olfactory sensory neurons (OSNs) encodes odor information. Here the authors report that odor-evoked inhibition in OSNs of Drosophila also encodes odor identity, and can in itself drive both attraction and avoidance behaviors.