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Quantitative systems-based prediction of antimicrobial resistance evolution.

Daniel A Charlebois1,2

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Predicting antimicrobial resistance evolution is possible using systems biology. Integrating quantitative models with experimental data offers a path to forecast microbial evolution and combat resistance.

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Area of Science:

  • Evolutionary biology
  • Microbiology
  • Systems biology

Background:

  • Antimicrobial resistance (AMR) is a complex, system-level challenge with significant public health implications.
  • Predicting the evolutionary trajectory of AMR is crucial for developing effective treatment strategies.
  • Understanding the predictability and repeatability of microevolutionary processes is key to forecasting AMR.

Purpose of the Study:

  • To explore the limits of predicting antimicrobial resistance evolution.
  • To quantitatively define the predictability and repeatability of microevolutionary processes.
  • To discuss the opportunities and challenges of predicting AMR within a systems biology framework.

Main Methods:

  • Review and synthesis of recent research on microbial evolution and AMR.
  • Quantitative definition of predictability and repeatability in microevolution.
  • Speculation on how these quantities vary across different scales (temporal, biological, complexity).

Main Results:

  • The evolution of antimicrobial resistance is a complex phenomenon.
  • Predictability and repeatability of microevolutionary processes can be quantitatively defined.
  • Systems biology approaches offer a framework for understanding AMR evolution.

Conclusions:

  • Antimicrobial resistance evolution can be predicted.
  • A systems biology approach, integrating quantitative models with multiscale experimental data, is essential for predicting AMR.
  • This approach holds promise for advancing the treatment of antimicrobial resistance.