A Multi-Modal View on Regulatory Network Rewiring in Disease
Event details
| Date | 23.09.2025 |
| Hour | 11:00 › 12:00 |
| Speaker | Prof. Vanessa Vermeirssen, Ghent University and Cancer Research Institute Ghent (CRIG), Belgium |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract:
Biography:
Vanessa Vermeirssen graduated from Ghent University with a Master of Science and a PhD in Bioscience Engineering. She conducted postdoctoral training in experimental and computational systems biology at the University of Massachusetts Medical School, the VIB-Center of Plant Systems Biology and the Center for Medical Genetics Ghent. She is currently an Associate Professor at Ghent University and a group leader at the Cancer Research Institute Ghent (CRIG), where she leads the Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR). Her aim is to unravel mechanisms of complex diseases by acquiring a functional understanding of gene regulation and signaling towards personalized medicine. Through gene regulatory networks, regulatory genomics, multi-omics data integration and single-cell data modeling, she intends to identify key regulators of disease and therapy resistance in cancer and neuro-inflammatory disorders.
I will cover different projects of the lab that range from (single-cell) multi-omics data modeling in cancer and neuro-inflammatory disorders to computational method development.
Several diseases are characterized by regulatory rewiring and metabolic reprogramming, with regulatory programs and key transcription factors and metabolites limitedly charted.
Bulk metabolomics data integration results in limited interpretability or restriction to known metabolites, while single-cell metabolomics is not technically mature yet. Here, we aimed to unravel metabolic programs in glioblastoma and inflammatory bowel disease through data-driven and interpretable multi-omics data integration, gene regulatory network inference and metabolic network modeling. We developed LemonIte, which associates regulatory metabolites and transcription factors to gene modules by integrating bulk-level transcriptomics and metabolomics data. Our methodology not only optimized multi-omics network inference for metabolites, but added biological interpretation through integration with an extensive gene-metabolite knowledge graph. At single-cell level, we modeled glioblastoma metabolism from single-cell transcriptomics data through metabolic pathway analysis, gene regulatory network inference and metabolic flux prediction. We identified high metabolic activity in tumor-associated macrophages across tumors and methodologies, but also pinpointed specific insights that can be gained from these different methodologies.
Several diseases are characterized by regulatory rewiring and metabolic reprogramming, with regulatory programs and key transcription factors and metabolites limitedly charted.
Bulk metabolomics data integration results in limited interpretability or restriction to known metabolites, while single-cell metabolomics is not technically mature yet. Here, we aimed to unravel metabolic programs in glioblastoma and inflammatory bowel disease through data-driven and interpretable multi-omics data integration, gene regulatory network inference and metabolic network modeling. We developed LemonIte, which associates regulatory metabolites and transcription factors to gene modules by integrating bulk-level transcriptomics and metabolomics data. Our methodology not only optimized multi-omics network inference for metabolites, but added biological interpretation through integration with an extensive gene-metabolite knowledge graph. At single-cell level, we modeled glioblastoma metabolism from single-cell transcriptomics data through metabolic pathway analysis, gene regulatory network inference and metabolic flux prediction. We identified high metabolic activity in tumor-associated macrophages across tumors and methodologies, but also pinpointed specific insights that can be gained from these different methodologies.
Vanessa Vermeirssen graduated from Ghent University with a Master of Science and a PhD in Bioscience Engineering. She conducted postdoctoral training in experimental and computational systems biology at the University of Massachusetts Medical School, the VIB-Center of Plant Systems Biology and the Center for Medical Genetics Ghent. She is currently an Associate Professor at Ghent University and a group leader at the Cancer Research Institute Ghent (CRIG), where she leads the Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR). Her aim is to unravel mechanisms of complex diseases by acquiring a functional understanding of gene regulation and signaling towards personalized medicine. Through gene regulatory networks, regulatory genomics, multi-omics data integration and single-cell data modeling, she intends to identify key regulators of disease and therapy resistance in cancer and neuro-inflammatory disorders.
Practical information
- General public
- Free
Organizer
- Prof. Bart Deplancke