My name is Swenja, and I am originally from Germany. As a bachelor student studying Biotechnology and Process Engineering in Flensburg, I soon realized that I enjoy the wet lab courses and biologic aspects of my studies the most. Determined to pursue a career in research, I thus chose to write my bachelor’s thesis at the Max Rubner-Institute in Kiel rather than in the industry. This was also when I first worked with bacteriophages, and they have grown to fascinate me ever since.
After my graduation, I continued to do a master’s degree in Biology-Biotechnology at the University of Copenhagen in Denmark. During this time, I further developed an interest in synthetic biology, which was first and foremost sparked by my participation in the iGEM competition in 2019. In my thesis, I was lucky to combine my interests by genetically engineer bacteriophages, aiming to enhance their efficacy for phage therapy.
For my PhD, I am excited to join the University of Zurich and the Balgrist University Hospital as ESR 7. During my project I will – in close collaboration with ESR 14 – aim to accelerate and enhance the selection process of phages with therapeutic potential.
Having concerned myself with human practice and some business basics next to my master’s studies, I have learned that finding the right solution to a problem often requires considering multiple perspectives. This is why I value in particular that the project combines scientific and computational aspects, while being close to the clinical side of phage therapy.
In a parallel universe, I probably own a café selling loads of cakes. I have a terrible sense of orientation. I enjoy outdoor activities and playing badminton.
Bacteriophages are the most abundant organisms on Earth, making it difficult to select which ones have the potential for therapeutic or other commercial applications. Current methods rely on non-iterative in vitro elaboration for product development, requiring >6 months per phage product.
In the current project, we aim to generate input data from experiments to build deep learning algorithms that will guide and support phage product development by
- revealing which phages compete best under which specific conditions and combinations,
- identifying mutations important for phage activity and survival under environmental conditions (pH, temperature), and
- demonstrating the population effects of phage infection in complex community structures.
Our group specialises in the AI-prediction of phage-host interactions in two-components systems (phage, bacteria, lysis/no lysis). In this project, we will use a top-down approach to characterise the resulting community composition and mutational spectra from in vitro assays and/or clinical samples to measure: 1. competition between multiple phages; 2. phage-interaction in complex communities to simulate microbiome and polymicrobial infection settings; and 3. exposition of phage to multiple environmental conditions using genomic re-sequencing and/or cycling temperature capillary electrophoresis (CTCE) to identify regions of mutations associated with specific stimuli.