Tuesday 17 March 2026

Host: Andrea Weisse

Speakers: Hollie Hindley (Centre for Engineering Biology) and Rodrigo Molina (CDT in AI for Biomedical Innovation). 

Title: Tackling antibiotic resistance through uncertainty quantification in a stochastic model of RNA repair. 

Abstract: Antibiotic resistance is a major global health threat. Understanding the mechanism of resistance is essential for developing strategies to combat it. An RNA-repair mechanism, the Rtc system, enables cells to rescue growth and survive treatment by conferring transient resistance to ribosome-targeting antibiotics, yet the mechanisms underpinning this resistance remain obscure. Observed cell-to-cell heterogeneity in E. coli rtc expression motivated a stochastic model of Rtc-regulated repair of translational RNAs. Here, we apply simulation-based inference to quantify the uncertainty around a key parameter of this model. Our results from two methods (rejection ABC with regression adjustment and neural posterior estimation) show little difference between the antibiotic-treated and untreated conditions. This motivates further work on model-guided experimental design to simulate the system more reliably and ultimately help design strategies for inhibiting Rtc-induced resistance.