Abstract
Antibiotic resistance is a growing public health problem. To gain a fundamental understanding of resistance evolution, a combination of systematic experimental and theoretical approaches is required. Evolution experiments combined with next-generation sequencing techniques, laboratory automation, and mathematical modeling are enabling the investigation of resistance development at an unprecedented level of detail. Recent work has directly tracked the intricate stochastic dynamics of bacterial populations in which resistant mutants emerge and compete. In addition, new approaches have enabled measuring how prone a large number of genetically perturbed strains are to evolve resistance. Based on advances in quantitative cell physiology, predictive theoretical models of resistance are increasingly being developed. Taken together, a new strategy for observing, predicting, and ultimately controlling resistance evolution is emerging.
Original language | English |
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Article number | 100365 |
Journal | Current Opinion in Systems Biology |
Volume | 28 |
DOIs | |
Publication status | Published - 1 Dec 2021 |
Externally published | Yes |
Funding
The authors thank Gerrit Ansmann and Theresa Fink for their feedback on the article. This work was supported in part by German Research Foundation ( DFG ) Collaborative Research Centre (SFB) 1310 and by a research fellowship of the Alexander von Humboldt Foundation (to YM).
Funders | Funder number |
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Alexander von Humboldt-Stiftung | |
Deutsche Forschungsgemeinschaft |