Integrated Regulatory Networks and Systems Biology
The CBIGR (pronounce “see bigger”) lab aims to unravel mechanisms of complex diseases by acquiring a functional understanding of gene regulation and signaling towards personalized medicine, using statistics, bioinformatics and machine learning, as well as high-throughput biological approaches. We are applying, benchmarking and optimizing methods in transcriptomics, (epi)genomics, single-cell analysis, multi-omics data integration, gene regulatory network inference and drug response prediction. The application focus of the lab is on disorders of the nervous system. In glioblastoma and neuroblastoma tumors, we identify novel driver genes and pathways, and we investigate plasticity and regulatory heterogeneity as responsible factors for drug escape and therapeutic failure. Tumors often show heterogeneity and the identification of key regulators that drive cancer transcriptional reprogramming can be used to therapeutically interfere in precision oncology. In neurological disorders, we study mechanisms of pan-neuroinflammation and gut-brain communication in search for novel preventive and therapeutic opportunities.
Areas of Expertise
- Biological networks
- Regulatory genomics, epigenomics and transcriptomics
- Single cell analysis
- Multi-omics data integration
- Bioinformatics and machine learning
Technology Transfer Potential
- Novel computational tools for (single cell) multi-omics data integration, gene regulation and biological network analysis
- Hypothesis generation on molecular mechanisms of disease and therapy failure
- Drug target identification through data-mining of high-throughput omics data
Selected publications
- Vandemoortele, B. & Vermeirssen, V. Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases. Eur J Neurol (2023). Visit ➚
- Vermeirssen, V. et al. Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA. NAR Cancer 4, zcac037 (2022). Visit ➚
- Loers, J. U. & Vermeirssen, V. SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks. BMC Bioinformatics 23, 363 (2022). Visit ➚
- Defoort, J., Van de Peer, Y. & Vermeirssen, V. Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant. Nucleic Acids Res 46, 6480-6503 (2018). Visit ➚
- Vermeirssen, V., De Clercq, I., Van Parys, T., Van Breusegem, F. & Van de Peer, Y. Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress. Plant Cell 26, 4656-4679 (2014). Visit ➚
Bibliography
- Full bibliography Visit ➚
