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. Author manuscript; available in PMC: 2018 Mar 20.
Published in final edited form as: J Antibiot (Tokyo). 2017 Sep 20;71(2):279–286. doi: 10.1038/ja.2017.108

Figure 2. Pipeline of quantitative experimental evolution to predict antibiotic resistance and identify targets for drug discovery.

Figure 2

Each stage produces essential data and approaches that when taken together predict resistance, identify the most important targets, suggests potential biochemical mechanisms, and leads to assay development. The stages are: 1) Evolve resistant populations in a bioreactor under polymorphic selection conditions; 2) Identify the frequency and order of mutations correlated with antibiotic resistance as a function of time; 3) Identify the genotypes of end-point isolates to establish genetic linkages; 4) Validate the effect of mutations by physicochemical characterization; 5) Rank candidates for potential drug development.