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The Evolutionary Systems Biology group at the University of Amsterdam is lead by Meike Wortel. The group uses mathematical tools and computational methods to understand microbial evolution, with a special focus on microbiomes and antibiotic resistance evolution. The group links these results with experimental approaches, in collaborations with experimental groups.
The group is embedded in the Microbiology department of the Swammerdam Institute of Life Sciences.
2024
September: Open postdoctoral position on Predicting evolution with Multiscale models: Apply before October 1st
August: Pietro Gadaleta started as a PhD student in the lab
July: Preprint by Pim van Leeuwen: "Modelling interactions that determine core gut microbiome stability to predict microbiome perturbation by opportunistic pathogens"
June: Abimbola Adekanmbi visits the lab for 5 months
May: Meike Wortel presented at the SMEEB conference
April: Meike Wortel obained a HFSP Research Grant to study the predictability of antifungal evolution of Candida species in communities of varying complexity together with the labs of Michael Manhart and Daniel Charlebois. The lab will soon be looking for a postdoc to work on multi-scale models of Candida, contact me if you are interested!
March: Meike Wortel will be presenting in the symposium organized for the PhD defense of Timo van Eldijk
January: Registration for the Summer school on Economic Principles in Cell Biology in Paris is open
2023
December: Meike contributed to review on Whole Cell Metabolic Control analysis in Biosystems.
September: Preprint on Conditions for coexistence of β-lactamase mutants
July: Bookchapter on "Metabolism in states of maximal efficiency" in open textbook on Economic Principles in Cell Biology
March: Review by Pim van Leeuwen on Synthetic microbial communities (SynComs) of the human gut in FEMS Microbiology Reviews
February: Paper on Evolutionary coexistence in fluctuating environments is out in Journal of Evolutionary Biology!
January: Review Towards evolutionary predictions: Current promises and challenges in Evolutionary Applications