Antibiotic resistance: Insights from evolution experiments and mathematical modeling

TL;DR

This paper discusses how combining experiments, sequencing, automation, and modeling advances our understanding of antibiotic resistance evolution, enabling better prediction and control strategies.

Contribution

It highlights recent integrative approaches that combine experimental data and mathematical models to study resistance development in bacteria.

Findings

Tracking stochastic dynamics of resistant mutants

Measuring resistance evolution propensity in genetically perturbed strains

Developing predictive theoretical models of resistance

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.