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Antibiotic treatments are sometimes not as effective against bacteria as we would want them to be. Bacterial populations make use of sensing and bet-hedging strategies to be least susceptible to the dynamic drug concentrations within their environment. If they do this successfully, they reserve time to develop antibiotic resistance. Because of this smart adaptation, both phenotypically and genetically, they are sometimes able to resume growth.
During the next years I will look more closely into this adaptation, using E.coli as a model organism. I am interested in the evolutionary constraints and will explore these by performing long-term experiments in dynamic antibiotic environments. Furthermore, I will aim for a more quantitative description of single-cell growth in such environments, to see how phenotypic subpopulations contribute to survivability, using time-lapse microscopy.
I obtained a bachelor’s degree in biology at Wageningen University. During the major ‘Organismal and Developmental Biology’, I became intrigued by the adaptability of organisms to their environment. I wanted to obtain a better molecular understanding of life – of organisms and their interactions. Therefore, I started the master ‘Bioinformatics and Systems Biology’, a collaboration between the two universities in Amsterdam.
During my master, I became more familiar with systems biology. It became clear to what extent we are currently able to obtain and process biological data, and how this abstraction is translated to intuitive knowledge. During my first internship in the Teusink group, I worked with a genome scale metabolic model of the cyanobacterium Synechocystis, to compute gene knockout strategies for biofuel production on solar energy. Thereafter, I performed an experimental internship on visualizing gene expression regulation and single-cell growth during carbon source transitions, with the use of time-lapse microscopy.