Hi, my name is Christian and I am german-born scientist currently living in Seattle. My primary areas of expertise are Data Science and Microbial Systems Biology, which means I am interested how all of the microbes, cells and molecules around and within us interact in order to maintain you happy and healthy. In particular, I want to know what happens if those interactions are perturbed, for instance by disease. I strongly believe that we can leverage medical, biological and environmental data to help us improve human health and to conserve our planet. We just have to embrace the inherent complexity.
I love everything that has to do with technology, particularly everything digital. As such I also like to experiment and play around with different hard and software. I enjoy sharing what I have learned and have taught many courses in academics or to tech professionals. If I am not doing any of that you will usually find me eating, cooking or baking.
I am currently a Postdoctoral Research Fellow at the Institute for Systems Biology and working in the field of Microbial Systems Ecology and Evolution. This is done within the Gibbons Lab under the leadership of Sean M. Gibbons.
In the last year(s) I switched my focus to the microbiome in the human gut which means I basically study the genetic material from the microbial communities that live within us. I am working with several data sets that study the microbial communities across thousands of individuals to identify the major changes in the microbiome during the transition from a healthy state to a diseased one. To that effect, I use various methods ranging from statistical inference to mathematical modeling in order to understand how the microbiome affects the host metabolism. I am particularly interested in methods that go beyond mere correlations but also do some wet lab work in order to validate computational predictions.
Before that I studied signaling in microbial cultures and metabolic alterations in cancer (you can find more about that in my publications).
In those days analyzing microbiome data often means dealing with read counts from high-throughput sequencing. Usually, we stratify those counts by some entity of interest: phyla, species, genes, sequence variants and so on. Three questions that may pop up when analyzing those read counts are the following: Does the sample contain enough reads to observe the majority of phyla, species, genes that are actually in the sample? How well do the reads represent their distribution in the sample?