Department Aquatic Ecology
Pathogene Evolution
Our research group focuses on understanding the evolution of drug resistance in diverse pathogens, from bacteria to parasitic worms. We work at the interface of evolutionary biology and pharmacology, developing mathematical models and collaborating with experimentalists to generate testable predictions. Our goal is to contribute to the development of more effective strategies for managing drug resistance in a One Health context.
The group was founded in 2024 with the support of a Starting Grant from the Swiss National Science Foundation. Our work draws on diverse fields, including population genetics, pharmacodynamics, and evolutionary computation, to create a comprehensive framework for understanding and predicting drug resistance evolution. Our main approaches are mathematical modelling and data collection and analysis.
Mathematical modelling
Despite the major biological differences between the pathogens, the fundamental evolutionary principles governing resistance evolution in all of them are the same: organisms experience selection pressure caused by the drug treatment, and those that acquire a mechanism to deal with it are more likely to survive and reproduce, passing it on.
Therefore, our group is developing a generalized, modular, computational framework to study drug resistance evolution that can be tailored to various organisms. Our flexible, modular framework allows us to identify the effects of these aspects, find commonalities and differences in drug resistance evolution across diverse taxa, adapt the model to capture the unique biological nuances of different organisms and determine the critical factors shaping drug resistance evolution.
Data collection and digitalization
Pharmacodynamic data are crucial in order to parameterize the models and generate concrete and testable predictions. The lack of a centralized, readily accessible repository for pharmacokinetic and pharmacodynamic (PKPD) data hinders efficient research and collaboration in the fight against drug resistance. While numerous databases exist, they are often fragmented, incomplete, and challenging to navigate. Crucially, a wealth of PKPD data remains hidden in supplementary materials or unpublished, making it incredibly difficult to access.
Our group aims to develop a comprehensive online platform to centralize and standardize PKPD data. We are extracting key parameters (e.g., growth rates, minimum inhibitory concentrations) from diverse sources, including existing databases and published literature, using advanced text mining and AI techniques. This curated information will be annotated, catalogued, and made freely available in a user-friendly, interactive database, benefiting researchers across disciplines.
Data analysis
By aggregating this data, we will be able to systematically analyze the availability, variability, and reliability of PKPD parameters, identifying critical knowledge gaps and emerging patterns across species, drugs, and environments. We aim to provide insight into the mechanisms driving these patterns and to identify critical factors that differentiate resistance evolution across species and drug classes.
Publications
2022 Trubenová, B. , Roizman D., Rolff, J., Regös, R.R. Modeling Polygenic Antibiotic Resistance Evolution in Biofilms. Frontiers in Microbiology : 13, 916035
2022 Szep, E. *, Trubenová, B. * , Csilléry, K. Using gridCoal to assess whether standard population genetic theory holds in the presence of spatio-temporal heterogeneity in population size. Molecular Ecology Resources: 22 (8), 2941-2955
2022 Trubenová, B. , Roizman D., Moter, A., Rolff, J., Regös, R.R. Population genetics, biofilm recalcitrance and antibiotic resistance evolution. Trends in Microbiology : 30 (9), 841-852
*Author contributed equally
