Antibiotic resistance is an increasingly serious public health concern. At the same time, this phenomenon
provides a rare opportunity to observe evolution in real time in the laboratory. Among the most challenging
open questions in this field is how resistance evolution can be predicted and how this worrisome process can be
slowed or perhaps even circumvented altogether. I will explain state-of-the-art techniques for investigating
resistance in evolution experiments, focusing on new techniques that use robots or custom-built experimental
setups that allow such experiments to be performed at high throughput while tightly controlling important
parameters. Using recent results from our work, I will illustrate how targeted perturbations can be used to
control spontaneous resistance evolution and how comprehensive measurements of the distribution of fitness
effects can be used to predict the dynamics of resistance evolution, at least under controlled conditions.