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

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.

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

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.

A model of pathogen co-evolving with host population continuously acquiring immunity is used to identify evolutionary parameters allowing pathogen population to persist without going extinct or splitting into independent lineages.

Hybrid brain network models predict neurophysiological processes that link structural and functional empirical data across scales and modalities in order to better understand neural information processing and its relation to brain function.