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 computational models of biological systems. The analysis of these models allows to gain insight into key properties of the investigated networks which builds the basis for designing promising experiments. Most group members have a dual background in molecular and computational biology. Our expertise covers genome-wide epigenomics approaches, data integration & reconstruction of molecular networks, predictive modelling applying network and machine learning approaches.

Epigenomics / Network Reconstruction

Most of the experimental and 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:

• Genome-wide Epigenomics / Transcriptomics approaches like RNA-seq, ChIP-seq, (single cell) ATAC-seq, and nanopore sequencing, including downstream data analysis (see Epigenetics team led by Dr. Lasse Sinkkonen)

• Gene regulatory network reconstruction via integration of transcriptomic and epigenomic data (30544251)

• Metabolic network reconstruction (24453953,31126892,26834640,26480823,26615025): FASTCORE algorithms

• Multiscale metabolic modelling (26904548,30787451)

• Signalling network reconstruction / Fast contextualization of logical networks (28673016,29872402,23815817,24983623): FALCON toolbox

Network Analysis / Machine Learning

Once large scale data are integrated in a network or machine learning model, these can be analyzed for systemic features and experimental design:

• Data Mining of human cohort data & Disease Risk Stratification (33244054)

• Metabolic network based Drug Repositioning (31126892,32369553)

• Signalling network based Drug Target Identification (30416750,30323272)

• Identification of Key Transcription Factors and Reprogramming Determinants (33173537,30544251, AlgoReCell)

• Integrated analysis of transcript-level regulation of metabolism (26480823,24198249)

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

Cancer specific Molecular Networks / Drug Discovery

By reconstructing and analysing cancer and subtype specific molecular networks, data integration is achieved and specific drug discovery is enabled, ultimatively aiming for personalized treatment. A specific interest lays in drug repositioning of established drugs for novel use in cancer treatment. We are contributing to fight:

• Melanoma (30416750,32410672,30323272,30240588,24675998,22815735)

• Colorectal cancer (31126892,31042485)

• Head and Neck cancer (31308358,30142511)

• Breast cancer (26631483)

Epigenetic Regulation in Cellular Differentiation and Disease

This research topic is the focus point of the Epigenetics team 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 networks to identify highly regulated nodes (26338775). Specific projects are:

• Lineage commitment of osteoblasts and adipocytes (30544251,24457907,31044623, AlgoReCell)

• Epigenetic regulation in Parkinson’s disease and related cell types (33173537,25852471,31621607,32248367)