A computational method for predicting evolutionary pathways to antimicrobial resistance, accounting for how epistatic interactions determine trajectories, is described.

Co-evolving residue pairs in the different components of a protein complex almost always make contact across the protein–protein interface, thus providing powerful restraints for the modeling of protein complexes.

Bacterial viruses are an unexpected ‘third party’ that imposes a strong predatory pressure on a bacterial pathogen during the natural course of infection in humans.

A combination of genetics, experimental evolution and mathematical modelling defines information necessary to predict the outcome of short-term adaptive evolution.

A theoretical model that considers the diversity of collateral effects among drug resistance mutations provides insights into the development of robust sequential antibiotic treatments.

An investigation of transposable element evolution in a group of long-term asexual animals shows that many of the predicted effects of asexuality are not observed.

Deep mining of GT-A fold sequences provides an evolutionary framework for investigating complex relationships connecting GT-A fold sequence, structure, function and regulation.

Mathematical modeling and data analysis show that, over evolutionary times, the yeast temporal program of replication strives to avoid stalling events and minimize interference between neighbor origins.

Physical interactions are a minor contributor to the correlations in evolutionary rate over time that are observed between co-functional proteins.

A population of pathogenic bacteria is found to respond to the loss of cooperation by privatising an essential function.