Christian Diener is tenure-track professor at Medical University of Graz and CoE Key Researcher Nr 31.
Christian obtained his Bachelor‘s degree in Bioinformatics and my PhD from the Free University of Berlin. During this time, he studied the intracellular signaling in haploid yest populations using a variety of computational techniques. He held a postdoctoral fellowship at the National Autonomous University of Mexico from 2012-2014 within the Institute of Cellular Physiology. In 2015 he joined the National Institute of Genomic Medicine in Mexico as Medical Researcher where he started his research on the human gut microbiome and its influence on human metabolic diseases with an emphasis on type 2 diabetes. This was followed by a move to Seattle, USA in 2018 for a position at the Institute for Systems Biology where he was promoted to Senior Research Scientist with a research focus on the human gut microbiome. In 2024, he joined the Medical University of Graz as an Assistant Professor of Computational Microbiome Science within the Cluster of Excellence.
His major contributions to the field have been to establish interactions between the human gut microbiome and human metabolism. He led projects that identified connections between the human gut microbiome and the human blood metabolome and its effects during weights loss and the onset of type 2 diabetes. He also participated in large interinstitutional projects that demonstrated the association of
the human gut microbiome with aging, metabolic health, and the efficacy of commonly prescribed drugs (specifically statins). During this research he led and collaborated on development of several methods and software tools such as COBRAPY, MICOM, QIIME2, and MEMOTE, which are now all widely used in the field.
As a Key researcher, his lab will contribute to the CoE by participating in WPs studying the small intestinal microbiome and fecal microbiota transplants (FMTs). Additionally, they will take a role in the synthesis module by suggesting standardized analysis strategies and core mechanisms that can be applied across work packages as well as providing expertise in metagenomic data analysis and metabolic modeling of complex microbial communities in diverse environments.