  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.
