Structured, not stirred: experimental evolution in programmable antibiotic landscapes

Given the looming antibiotic resistance crisis, it is imperative to understand the fundamental determinants of resistance evolution dynamics.

When antibiotics are administered to a patient, imperfect drug penetration leads to a highly inhomogeneous concentration profile. In contrast, evolution experiments to study resistance are commonly performed in homogeneous liquid environments.

Recently, there has been a surge of theoretical studies suggesting that spatial drug concentration gradients drastically accelerate resistance evolution as resistant mutants evade resource competition by invading drug-enriched territory.

However, the effect of spatial structure on evolutionary dynamics remains unclear due to a lack of experimental assays with well-defined, inhomogeneous landscapes.Here, I propose to develop a novel experimental assay where bacteria migrate and evolve on a landscape consisting of hundreds of independently definable environments.

This assay will be implemented using custom liquid handling and imaging robots to systematically program antibiotic landscapes and measure evolution on a wide range of drug environments.

By combining this novel assay with theoretical modeling, I will investigate evolution in monotonous and rugged drug landscapes.

Motivated by theoretical predictions that multi-drug strategies aimed at preventing resistance evolution become compromised in inhomogeneous environments, I will further investigate the effect of spatial inhomogeneity using defined multi-drug landscapes.

This project will provide a unique, quantitative and systematic experimental investigation on how spatial structure affects evolutionary dynamics. Beyond bacteria evolving antibiotic resistance, the same concepts apply for tumor resistance evolution.

Overall, this project will greatly advance our understanding of resistance evolution with broad biomedical implications.