English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Antimicrobial resistance is an emerging global health crisis that isundermining advances in modern medicine and, if unmitigated, threatens to kill10 million people per year worldwide by 2050. Research over the last decade hasdemonstrated that the differences between genetically identical cells in thesame environment can lead to drug resistance. Fluctuations in gene expression,modulated by gene regulatory networks, can lead to non-genetic heterogeneitythat results in the fractional killing of microbial populations causing drugtherapies to fail; this non-genetic drug resistance can enhance the probabilityof acquiring genetic drug resistance mutations. Mathematical models of genenetworks can elucidate general principles underlying drug resistance, predictthe evolution of resistance, and guide drug resistance experiments in thelaboratory. Cells genetically engineered to carry synthetic gene networksregulating drug resistance genes allow for controlled, quantitative experimentson 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 ourunderstanding of antimicrobial resistance to discover effective therapiesagainst drug-resistant infections.

Advancing Drug Resistance Research Through Quantitative Modeling and Synthetic Biology