Farquhar Kevin S, Flohr Harold, Charlebois Daniel A
Precision for Medicine, Houston, TX, United States.
Department of Physics, University of Alberta, Edmonton, AB, Canada.
Front Bioeng Biotechnol. 2020 Sep 18;8:583415. doi: 10.3389/fbioe.2020.583415. eCollection 2020.
Antimicrobial resistance (AMR) 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 AMR to discover effective therapies against drug-resistant infections.
抗菌药物耐药性（AMR）是一场正在浮现的全球健康危机，正在破坏现代医学的进展，并且如果不加以缓解，到2050年全球每年可能有1000万人因此丧生。过去十年的研究表明，相同环境中基因完全相同的细胞之间的差异会导致耐药性。由基因调控网络调节的基因表达波动会导致非遗传异质性，从而导致微生物群体部分死亡，使药物治疗失败；这种非遗传耐药性会增加获得遗传耐药性突变的可能性。基因网络的数学模型可以阐明耐药性背后的一般原理，预测耐药性的演变，并指导实验室中的耐药性实验。经过基因工程改造以携带调节耐药基因的合成基因网络的细胞，能够针对非遗传异质性在耐药性发展中的作用进行可控的定量实验。在这篇观点文章中，我们强调数学、计算和合成基因网络模型在推动我们对抗菌药物耐药性的理解以发现针对耐药性感染的有效疗法方面所做出的贡献。
