Systems Biology

The research of our group is focussed in the area of molecular systems biology, particularly in:

Model based Data Integration and Analysis of Disease specific Molecular Networks

We are developing modelling and data integration techniques to generate suitable computational models of biological systems. The analysis of these models helps to gain insight into dynamic and stationary properties of the investigated networks which gives the basis for suggesting further promising experiments. The area of expertise of the SysBio members covers detailed dynamical modelling of signaling networks, qualitative (probabilistic) Boolean, constraint based modeling of metabolic networks, and model based data integration. Most group members have a dual background in biology/ medicine and computational systems biology.

Tool development

Most of the computational tools we are developing are applicable to a wide range of biological systems and processes. Ongoing more general work in the different applications comprises:

•  Compact metabolic network reconstruction

•  Network curation using Boolean and Probabilistic Boolean modelling

•  Omics data integration and vizualization

•  Global sensitivity analysis / Early warning

The current bio-medical applications (in close collaboration with biological experts) are:

Mammalian signaling networks

One main focus lays in the development and analysis of mathematical models of pro- and anti-apoptotic signalling in mammalian cells. We are interested in understanding and influencing the signalling protein networks that govern the decision between life and programmed cell death. Furthermore we focus on deciphering the characteristics of disease related PDGF signaling networks. Specific projects are:

• Modelling and analysis of the NFкB-dependent UVB induced apoptosis in melanoma

• Evaluation of influences of 1,25(OH)2VitaminD3 on NFкB signalling in atherosclerosis

• Identification of perturbation-sensitive targets and disease markers in PDGF signaling related diseases

Integrated modelling and epigenetic regulation of metabolism

We are developing computational tools and generating high-throughput data sets for integration with constraint-based models of metabolic networks to identify highly regulated nodes of metabolic networks in processes such as disease and cellular differentiation. In a proof of principle study we integrated transcriptomics and metabolomics data with a model of human metabolism and genome-wide identification of metabolic target genes of key transcription factors and microRNAs to identify disease-associated nodes in adipocyte differentiation. Individual nodes are further analyzed with more quantitative models of combinatorial gene regulation by multiple regulators. In collaboration with the Luxembourg Centre for Systems Biomedicine (LCSB) we are currently extending these efforts towards the fast reconstruction of context-specific metabolic models. The unbiased identification of the context-specific key regulators is achieved through epigenomic analysis using methods such as ChIP-Seq and GRO-Seq to reveal genomic features such as cell type-specific enhancers. Specific projects include:

• Integrated metabolic modelling of white and brown adipogenesis

• Integrated metabolic modelling of the monocyte / macrophage / foam cell differentiation in atherosclerosis

• Integrated modelling and epigenetic regulation of metabolism in iPS cell-derived neural stem cells in context of Parkinson’s disease

• Combinatorial gene regulation by multiple microRNAs during adipocyte differentiation

• Role and regulation of non-coding RNAs during early lineage commitment of osteoblasts and adipocytes