Advancing Drug Resistance Research Through Quantitative Modeling and Synthetic Biology

TL;DR

This paper discusses how mathematical modeling and synthetic biology can improve understanding of antimicrobial resistance, aiding the development of effective therapies against drug-resistant infections.

Contribution

It highlights the role of gene network models and synthetic gene circuits in studying non-genetic heterogeneity and resistance evolution.

Findings

Mathematical models predict resistance development pathways.

Synthetic gene networks enable controlled experiments.

Models guide effective drug therapy strategies.

Abstract

Antimicrobial resistance is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of antimicrobial resistance to discover effective therapies against drug-resistant infections.