Project description DEENESFRITPL Modelling of the evolution of bacterial antibiotic resistance Bacterial antibiotic resistance is an enormous public health challenge. The coexistence of antibiotic-resistant and sensitive genotypes within the same population is still an unresolved problem. Current epidemiological models predict the dominance of either of the two strains or suffer from generality. The EU-funded PolyPath project aims to resolve this by coupling within-host pathogen dynamics and between-host transmission of bacteria. Modelling the within-host system will enable the prediction of the rate of resistance emergence and abundance of sensitive and resistant bacteria with or without antibiotic treatment. The resistant bacteria thrive under antibiotic treatment, while the sensitive strain has an advantage in invading and colonising untreated hosts. The project will help to identify the optimal treatment strategies to solve the antibiotic resistance. Show the project objective Hide the project objective Objective Understanding and controlling the evolution of antibiotic resistant strains is one of the biggest public health challenges of our time. Despite a vast amount of data gathered and models being developed, coexistence of antibiotic resistant and sensitive genotypes within the same bacterial pathogen is still an unresolved problem. Simple epidemiological models predict the dominance of either of the two strains while more complex models suffer from generality. Using empirical evidence, I set out to resolve this problem by coupling within-host pathogen dynamics and between-host transmission of bacteria. First, stochastically modelling the within-host system I will develop predictions for the rate of resistance emergence and abundance of sensitive and resistant individuals in hosts with or without antibiotic treatment. While resistant bacteria thrive under antibiotic treatment, the sensitive strain has an advantage in invading and colonising untreated hosts. The outcomes help to get a more detailed understanding of the within-host dynamics, e.g. identification of optimal treatment strategies to confine the evolution of antibiotic resistance. Feeding these results into the dynamics on the population level, the between-host level, will result in a within-between-host feedback. Fitting and confronting the model to empirical data on prevalence and resistance emergence in Streptococcus pneuomoniae and Escherichia coli will conclude this project. The mechanistic implementation of the dynamics can immediately be linked to data which is of great importance given the increasing amount of empirical studies in the field of epidemiology. Through the theoretical and applied results, the study will add new insights and predictions in the field of infectious disease evolution and be able to identify factors enabling the stable coexistence of antibiotic resistant and sensitive bacteria. Fields of science natural sciencesbiological sciencesmicrobiologybacteriologymedical and health scienceshealth sciencespublic healthepidemiologymedical and health scienceshealth sciencesinfectious diseasesmedical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsantibioticsmedical and health sciencesbasic medicinepharmacology and pharmacydrug resistanceantibiotic resistance Keywords PolyPath Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2018 - Individual Fellowships Call for proposal H2020-MSCA-IF-2018 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator SORBONNE UNIVERSITE Net EU contribution € 184 707,84 Address 21 rue de l'ecole de medecine 75006 Paris France See on map Region Ile-de-France Ile-de-France Paris Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
