Vermeirssen Unit: Lab for Computational Biology, Integromics and Gene Regulation (CBIGR)

Research Field: Integrated Regulatory Networks / Systems Biology

Group leader: Prof. Dr. Ir. Vanessa Vermeirssen

Tel: +32 9 331 35 46

Research topic

Technological advancements have led to a wealth of omics data in bulk, but also more and more at single cell level: genome, epigenome, transcriptome, proteome, metabolome, ... The capacity to integrate multi-omics data, to something more than the sum of the existing data, is key to elucidating the causes of complex diseases such as neuro-inflammatory diseases and cancer. Regulatory networks are a valuable tool to map systems-wide the relationships between DNA, RNA, proteins and metabolites and to understand the molecular mechanisms by which these molecules influence health and disease. Our research focuses on acquiring a functional understanding of gene regulation and signaling through multi-omics data integration and regulatory network inference, with specific attention to the impact of the environment. To this end, we develop and apply computational approaches involving statistics, bioinformatics and machine learning. Our ultimate goal is to use this knowledge in the development of novel preventive or therapeutic strategies for successful personal healthcare. We are embedded in a multidisciplinary consortium of the inflammatory gut-brain axis where we aim to unravel the molecular mechanisms of gut-brain communication in neuro-inflammatory disorders. We are also involved in cancer research, where our goals are to identify novel driver genes and pathways, characterize tumors and the interaction with their environment, and contribute to personalized medicine.

Our multi-omics data integration framework based on network motif clustering is able to detect previously known and novel regulators of modules. In the figure above, cellulose synthase complexes (CSC) in COMc 36 are upregulated by MYB46 upon salt stress and brassinosteroid treatment in Arabidopsis thaliana.

Areas of expertise

  • Biological networks
  • Network inference
  • Multi-omics data integration
  • Gene regulation bioinformatics

Technology transfer potential

  • Novel computational tools for multi-omics data integration and biological network analysis
  • Hypothesis generation using multi-omics data integration and biological network analysis
  • Drug target identification through data-mining of high-throughput omics data

Selected publications

  1. Defoort J. et al. Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant.
    Nucleic Acids Res. 46(13):6480-6503. 2018.
  2. Vermeirssen V. et al. Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.
    Plant Cell. 26(12):4656-79. 2014.
  3. Vermeirssen V. et al. Transcription factor modularity in a gene-centered C. elegans core neuronal protein-DNA interaction network.
    Genome Research. 17(7):1061-71. 2007.

To top