An evolutionary modeling approach to combating antibiotic resistance

European Research Council-funded project will study E. coli spread with both computer simulations and experiments.
 Low-temperature electron micrograph of a cluster of E. coli bacteria, magnified 10,000 times. Each individual bacterium is oblong shaped. Image: public domain via Wikimedia Commons
Low-temperature electron micrograph of a cluster of E. coli bacteria, magnified 10,000 times. Each individual bacterium is oblong shaped. Public domain via Wikimedia Commons

Escherichia coli (E. coli) bacteria are globally the most frequent cause of urinary tract infections and bloodstream infections in humans. Rising levels of antibiotic resistance over the past 20 years has further increased the public health burden resulting from such infections.

A new project called ACES, funded by the European Research Council’s Advanced Grant, will study exactly how E. coli bacteria compete when they are colonizing the human gut. Asymptomatic gut colonization always precedes these types of infections, and scientists currently have a poor understanding of this process. ACES aims to close this knowledge gap and contribute to better prediction of how novel antibiotics, probiotics or vaccines would affect the population of bacteria like E. coli.

“To develop new solutions to the ongoing global antibiotic resistance crisis, we need to look beyond the current toolbox and find ways to identify the first principles behind colonization competition success,” says professor Jukka Corander. ACES will combine natural population experiments and the latest genome sequencing technology with in vivo experiments and agent-based evolutionary game-theoretic modelling to significantly advance understanding about E. coli competition. 

ACES is the third European Research Council (ERC) large-scale funding granted to Jukka Corander, who is jointly based at the University of Oslo and the Wellcome Sanger Institute in Cambridge, UK. Corander, also a principal investigator at ELLIS Institute Finland, says that simulation-based inference methods spearheaded by the ELLIS Institute and the Finnish Center for Artificial Intelligence FCAI will play a central role in calibrating the predictive models developed in ACES.

Contact:

Jukka Corander

Jukka Corander, PI (external link)

Evolutionary epidemiology, Machine learning, Inference

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