Quantitative systems-based prediction of antimicrobial resistance evolution

Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.

Automated summary

Predicting evolution is a fundamental problem in biology, particularly in treating antimicrobial resistance, a complex system-level phenomenon. This perspective article explores the limits, predictability, and repeatability of microevolutionary processes, and discusses the opportunities and challenges for predicting antimicrobial resistance in the context of systems biology. Recent research suggests that a systems biology approach integrating quantitative models and multiscale data can predict the evolution of antimicrobial resistance.