Systems Biology 

The Computational Systems Biology group is part of the Centre for Systems Biology and Bioenergetics (CSBB) and focuses on the development of large-scale mechanistic models of the cell with molecular detail. Our questions cover two main research lines: i) understanding the systems-level effects of genetic disorders in energy metabolism with the final goal to design affective treatments and ii) evolutionary systems biology to understand the underlying evolutionary trajectories of adaptation with the ultimate aim to predict evolution.

In our research on genetic (energy) disorders we construct detailed metabolic models of mouse and human tissue cells on the basis of their genome sequence, relevant literature and comparative genomics. To analyze the consequences of genetic disorders we use in-house generated large-scale experimental data such as the expression profiles of genes. Furthermore, in collaboration with our experimental partners we aim to measure other important factors like the rate of nutrient uptake and in vivo enzyme fluxes. All this experimental data is integrated into the metabolic network to form computational models that are tissue-specific. Using these models we aim to distinguish healthy and disorder states to increase our understanding of the origin and effects of genetic diseases. Importantly, in all our projects we emphasize on a strong combination between i) the development of sophisticated computational algorithms to manage large amounts of data and to analyze our developed molecular models, and ii) addressing important biological questions, such as why are genetic disorders eventually lethal? In any of our analysis there is a strong interaction with experimental groups of the CSBB to evaluate our predictions.

In the second research line we focus on evolutionary systems biology where we develop mechanistic models to address important questions of evolution. It is clear that the prediction of evolutionary events is difficult, since it requires a detailed knowledge of the distribution of mutations and their fitness effects. In our view, this can be best addressed by combining theories of evolution, mechanistic models and molecular data. We use metabolic models of S. cerevisiae and E. coli and integrate these with comparative genome approaches, such as those that infer enzyme ancestral states, to explain (general) patterns in genome evolution. In other words, we address the question how the genomes of species are shaped over time? Besides explaining evolutionary events of the past, we also intend to explore the possibility to predict evolution by using metabolic models. In other words, can we predict which genes are likely to get mutated and/or of which its regulation get changed? The ultimate goal of our research is to gain a global understanding of why particular evolutionary events are realized, and others not. Our work is therefore of great importance in the field of medicine for the prediction of antibiotic targets and the species ability to evolve resistance. All our work regarding the development of computational models in the light of evolution are directly linked to experimental work by our international collaborators Csaba Pal and Balazs Papp from the Biological Research Centre, Szeged, Hungary and Cambridge University