Department Computational Biology
In the Department Computational Biology, we focus on the development, application, and integration of bioinformatics, systems biology, and data science approaches. We aim to advance mechanistic understanding in toxicology and environmental health and to establish predictive methods using this knowledge.
More specifically, we aim at
1) Combining bioinformatics and systems biology methods to unravel adverse mechanisms
- (Multi)-omics data integration to link to or refine Adverse Outcome Pathways (AOPs)
- Predict adverse effects of chemical mixtures using pathway knowledge
- Use single-cell omics to unravel cell-type-specific regulatory programs and effects
- Investigate the role of non-protein coding RNAs in adverse processes
2) Meeting and promoting the principles of Reproducible Research
3) Developing and applying artificial intelligence approaches in bioinformatics and predictive toxicology
- Implement and use Deep Learning methods, like graph neural networks (GNN), variational (adversarial) autoencoders (VAE), long short-term memory networks (LSTM)
- Introduce Explainable AI and Quantification of Uncertainty in our approaches
4) Developing knowledge representation, data integration and – enrichment approaches
- Enable prediction and assessment at the Chemical Universe level
- Generate hypotheses on substances or mixtures of concern for monitoring, environmental epidemiology, and proof of concept studies