The Computational Biology group addresses biological problems using computational, mathematical and biophysical methods. We want to understand how cellular and molecular systems adapt to their host environments and how they change in evolution.
Research Axis 1: we are fascinated by mitochondria and their metabolism and want to understand how they are able to adapt to their cellular host environment. We study mitochondrial diversity using several computational methods, such as data integration, visual data mining, network biology, metabolic modelling and deep learning.
Research Axis 2: we want to understand how eukaryotic cells choose their origin of replication to duplicate their genomes. We use several approaches to study this question, ranging from data modelling, deep learning to molecular dynamics simulations.
Research Axis 3: we are exited about evolution and how proteins and organisms adapt to their environment, for instance by evolving novel traits or by readjusting their metabolism We study this phenomenon in close collaboration with experimental teams and apply methods such as classical sequence analysis and phylogenetics, single-cell based data analysis, to comparative metabolic modelling.