Prof Ines Thieleis the principal investigator of the Molecular Systems Physiology group at the National University of Ireland, Galway. Her research aims to improve the understanding of how diet influences human health. Therefore, she uses a computational modelling approach, termed constraint-based modelling, which has gained increasing importance in systems biology. Her group builds comprehensive models of human cells and human-associated microbes; then employs them together with experimental data to investigate how nutrition and genetic predisposition can affect ones health. In particular, she is interested in applying her computational modelling approach for better understanding of inherited and neurodegenerative diseases. Ines Thiele has been pioneering models and methods allowing large-scale computational modelling of the human gut microbiome and its metabolic effect on human metabolism. Prof Ines Thiele earned herPhDin bioinformatics from the University of California, San Diego, in 2009. From 2009 until 2013, Ines Thiele was an Assistant Professor at the University of Iceland. From April 2013 until January 2019, she was an Associate Professor at the University of Luxembourg. Since February 2019, Ines Thiele is a Professor for Systems Biomedicine at the National University of Ireland, Galway. In 2013, Ines Thiele received the ATTRACT fellowship from the Fonds National de la Recherche (Luxembourg). In 2015, she was elected as EMBO Young Investigator. In 2017, she was awarded the prestigious ERC starting grant. She is an author of over 80 international scientific papers and reviewer for multiple journals and funding agencies.

The Molecular Systems Physiology group, headed by Prof. Ines Thiele, aims to improve our understanding of how diet influences human health.We use a computational modelling approach, termed constraint-based modelling, which has gained increasing importance in systems biology. In this approach, comprehensive computational models are assembled in a bottom-up manner from literature and genomic information. These models describe in a stoichiometric accurate format biochemical transformations occurring in a target organism. Once assembled, various omics data sets can be integrated and analyzed with these models, expanding currently available analysis tools and thus providing mechanistically based insight into complex multi-dimensional data sets. Moreover, these models can be used to predict the impact of genetic alterations (e.g., enzyme deficiencies) and of changed environment conditions (e.g., changes in diet composition) on the metabolic state of the target organism. The molecular systems physiology group builds comprehensive models of human cells and human-associated microbes. We then employ these models together with experimental data to investigate how nutrition and genetic predisposition can affect ones health. In particular, we are interested in applying our computational modelling approach for better understanding of inherited and neurodegenerative diseases.

Systems Biology; Constraint-based metabolic modelling