Computational Systems Biology

Group leader
Computational Systems Biology
Specific themes and goals

The Computational Systems Biology group at Constructor University works at the interface of statistical physics, systems biology and systems medicine. Our investigations simultaneously cover ‘omics’ data interpretation in biology and medicine and theoretical studies of dynamics on graphs. 

We have pioneered the analysis of gene expression data via network coherences (small quantifiers of the match between a network and an expression profile), which has allowed us to better understand the interplay of chromosomal organization and transcriptional regulatory networks in bacterial gene regulation. 

Our work on network-based analyses of transcriptome data from clinical cohorts has led to new insight into various diseases, such as primary hyperaldosteronism, chronic inflammatory bowel diseases, and hepatocellular carcinoma. 

We have discovered self-organized excitation waves around hubs in complex networks and explored the implications of this finding for neuroscience. 

Furthermore, we used the notion of multilayer networks to identify a simple fundamental principle, how a biological system can balance the conflicting requirements of sensitivity and robustness. 

A large body of our work across the years focuses on interdisciplinary applications of concepts from the theory of complex systems and from network science (including, for example, long-distance train connections, industrial production, coauthorship networks, psychotherapy dynamics, and networks of scientific hypotheses).

Highlights and impact

Starting from the foundation of dynamical processes on graphs, the Computational Systems Biology group has become a prominent research unit at the interface of statistical physics, data science and biology. The group’s achievements between 2019 and 2022 include: 

  • In the EU-funded project i-CONN, we have studied how network architecture shapes dynamics across a wide range of network architectures and dynamical processes. We found two distinct types of structure-function relationships (Voutsa et al., 2021) and discussed their relevance in various disciplines.
  • In a joint work with Paolo Moretti at the University Erlangen-Nürnberg, we achieved a theoretical understanding of self-organized waves on graphs (Moretti and Hütt, 2020). 
  • In collaboration with researchers from France, we have found that genetic point mutations (single nucleotide polymorphisms) in cancers follow different statistical distributions with respect to chromosomal domains compared to non-cancer diseases (Jablonski et al., 2022). 
  • In an ongoing industry-funded project on cocoa metabolomics COMETA (which is a joint project with the research groups of Matthias Ullrich and Nikolai Kuhnert at Constructor University), we have studied how the chemical composition of cocoa beans allows prediction of origin countries and fermentation status (Kumar et al., 2021).
  • The BMBF-funded project sysINFLAME brought together clinical researchers with systems biologists, as well as researchers from medical informatics and medical statistics. The Computational Systems Biology group has designed a generator of transcriptomics data for disease cohorts based on concepts from statistical physics.
  • In collaboration with colleagues from Universitätsklinikum Eppendorf (UKE), we have discovered via simulated evolution a potential origin of the modularity of neuronal structures (Damicelli et al., 2019).
Group composition & projects/funding

Between 2019 and 2022, the Computational Systems Biology group consisted of seven to 10 PhD students and two postdocs. We obtained funding from the German Ministry of Education and Research (BMBF), the German Research Foundation (DFG), the Volkswagen Foundation, the European Union within the framework of an Innovative Training Network (ITN), and several partners from industry.

Selected publications
  • Jablonski, K.P., Carron, L., Mozziconacci, J., Forné, T., Hütt, M.-Th. and Lesne, A. (2022) Contribution of 3D genome topological domains to genetic risk of cancers: a genomewide computational study. Human Genomics 16(1), 1–15.
  • Kumar, S., D’Souza, R. N., Corno, M., Ullrich, M. S., Kuhnert, N., and Hütt, M.-Th. (2022). Cocoa bean fingerprinting via correlation networks. npj Science of Food 6(1), 1–9.
  • Voutsa, V., Battaglia, D., Bracken, L.J., Brovelli, A., Costescu, J., Diaz Munoz, M. Fath, B.D., Funk, A., Guirro, M., Hein, T., Kerschner, C., Kimmich, C., Lima. V., Messé, A., Parsons, A.J., Perez, J., Pöppl, R., Prell, C., Recinos, S., Shi, Y., Tiwari, S., Turnbull, L., Wainwright, J., Waxenecker, H. and Hütt, M.-Th. (2021) Two classes of functional connectivity in dynamical processes in networks. Roy. Soc. Interface 18(183), 20210486.
  • Moretti, P. and Hütt, M.-Th. (2020) Link-usage asymmetry and collective patterns emerging from rich-club organization of complex networks. PNAS 117,18332–18340.
  • Damicelli, F., Hilgetag, C.C., Hütt, M.-Th. and Messé, A. (2019) Topological reinforcement as a principle of modularity emergence in brain networks. Network Neuroscience, 3, 589–605.