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 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. Most group members have a dual background in biology/ medicine and computational systems biology. Their expertise covers detailed modelling of cancer signaling networks, reconstruction of context specific metabolic networks, clinical data analysis and genome-wide epigenetic analysis.

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 (PMIDs:24453953,26480823,26834640,26615025)

• Multiscale modelling of disease specific and drug metabolism (integration with PBPK-modelling) (PMID:26904548)

•  Network curation using Probabilistic Boolean modelling (PMIDs:23815817,24983623,26631483,H2020 Initial Training Network project)

•  Omics data integration and vizualization (PMIDs:24198249,26338775)

•  Data Mining / Machine Learning (PMID:26154857)

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

Cancer specific signaling networks and multi-scale modeling of cancer

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. Specific projects are:

• Modelling and analysis of the NFкB-dependent UVB induced apoptosis in melanoma (PMIDs:24675998,22815735,19607706)

• Predicting individual sensitivity of malignant melanoma to combination therapies by statistical and network modeling on innovative 3D organotypic screening models (e:Med Systems Medicine project)

• Establishing a multi-scale modelling environment for pro-survival and proliferation signalling to understand melanoma growth arrest and recurrence (H2020 Initial Training Network project)

• Alterations of PDGF signaling in Gastrointestinal stromal tumours (GIST) (PMIDs:26413425,25880691)

• L-plastin signalling in breast cancer (PMID:26631483)

Integrated modelling and epigenetic regulation of metabolism

This research topic is one of the focus points of the Epigenetics team led by Dr. Lasse Sinkkonen that works on the understanding of epigenetic mechanisms and regulators, both at transcriptional and post-transcriptional level, that determine cell identity in cellular differentiation and disease in mammalian cells. We are also thereby 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. Specific projects are:

• Integrated metabolic modelling of adipogenesis (PMID:24198249)

• Role and regulation of non-coding RNAs during early lineage commitment of osteoblasts and adipocytes (PMID:24457907)

• Integrated modelling and epigenetic regulation of metabolism in iPS cell-derived neural stem cells in context of Parkinson’s disease (PMID:25852471)

Data mining of human clinical and cohort data

We are also appling integrative network and machine learning approaches for analyzing multi-level and large-scale data sets from clinical contexts. Specific projects are:

• An Integrative Systems Medicine Approach to Mapping Human Metabolic Diseases (MetaEaseMap) (PMID:26154857)

• Cardiovascular risk prediction employing machine learning (LURIC - LUdwigshafen Risk and Cardiovascular Health Study cohort)