A theory is presented for the optimal design of cellular sensing systems that maximizes the sensing precision given resource constraints.

A model that simulates realistic fly walking uses an anatomically-inspired layered architecture to achieve robust walking, quantifying the tolerance of this circuit to sensorimotor delays.

The onset of habituation phenomena enables accurate processing of external stimuli when biological systems adapt their parameters to balance dissipation and the information between input and response.

A principled statistical segmentation of fruit fly walking leads to a compact model of immediate actions that can reproduce the unique behavioral sequences of individual flies.

Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.

The ParABS system positions plasmids precisely within the cell but just below the threshold for oscillatory dynamics.

Stochastic models able to reproduce both incidence and seroprevalence data for Zika outbreaks in the Pacific Islands estimate that the basic reproduction number (R₀) is between 1.5 and 4.1.

The statistics of binocular rivalry at different combinations of image contrast is reproduced quantitatively by competing out-of-equilibrium populations of independent neural assemblies with idealized attractor dynamics.

A computational framework enables the inference of hydrodynamic continuum models for collective cell migration from live-cell imaging data recorded in zebrafish embryos.

A novel computation tool for microbial community modeling predicts the evolution and diversification of E. coli in laboratory evolution experiments and gives insight into the underlying metabolic processes.