Antibiotic resistance can emerge spontaneously through genomic mutation and render treatment ineffective. To counteract this process, in addition to the discovery and description of resistance mechanisms, a deeper understanding of resistance evolvability and its determinants is needed. To address this challenge, this thesis uncovers new genetic determinants of resistance evolvability using a customized robotic setup, explores systematic ways in which resistance evolution is perturbed due to dose response characteristics of drugs and mutation rate differences, and mathematically investigates the evolutionary fate of one specific type of evolvability modifier - a stress induced mutagenesis allele. We find several genes which strongly inhibit or potentiate resistance evolution. In order to identify them, we first developed an automated high-throughput feedback-controlled protocol which keeps the population size and selection pressure approximately constant for hundreds of cultures by dynamically re-diluting the cultures and adjusting the antibiotic concentration. We implemented this protocol on a customized liquid handling robot and propagated 100 different gene deletion strains of Escherichia coli in triplicate for over 100 generations in tetracycline and in chloramphenicol, and compared their adaptation rates. We find a diminishing returns pattern, where initially sensitive strains adapted more compared to less sensitive ones. Our data uncover that deletions of certain genes which do not affect mutation rate, including efflux pump components, a chaperone and several structural and regulatory genes can strongly and reproducibly alter resistance evolution. Sequencing analysis of evolved populations indicates that epistasis with resistance mutations is the most likely explanation. This work could inspire treatment strategies in which targeted inhibitors of evolvability mechanisms will be given alongside antibiotics to slow down resistance evolution and extend the efficacy of antibiotics. We implemented a stochastic population genetics model, to verify ways in which general properties, namely, dose-response characteristics of drugs and mutation rates, influence evolutionary dynamics. In particular, under the exposure to antibiotics with shallow dose-response curves, bacteria have narrower distributions of fitness effects of new mutations. We show that in silico this also leads to slower resistance evolution. We see and confirm with experiments that increased mutation rates, apart from speeding up evolution, also lead to high reproducibility of phenotypic adaptation in a context of continually strong selection pressure. Knowledge of these patterns can aid in predicting the dynamics of antibiotic resistance evolution and adapting treatment schemes accordingly. Focusing on a previously described type of evolvability modifier – a stress-induced mutagenesis allele – we find conditions under which it can persist in a population under periodic selection akin to clinical treatment. We set up a deterministic infinite population continuous time model tracking the frequencies of a mutator and resistance allele and evaluate various treatment schemes in how well they maintain a stress-induced mutator allele. In particular, a high diversity of stresses is crucial for the persistence of the mutator allele. This leads to a general trade-off where exactly those diversifying treatment schemes which are likely to decrease levels of resistance could lead to stronger selection of highly evolvable genotypes. In the long run, this work will lead to a deeper understanding of the genetic and cellular mechanisms involved in antibiotic resistance evolution and could inspire new strategies for slowing down its rate.