Causal Systems Immunology

Our group is part of VIB.AI Center for AI and Computational Biology, while being affiliated with the VIB-UGent Center for Inflammation Research. See wsaelenslab.sites.vib.be for our full website.

Our lab uses advanced probabilistic AI models to solve complex immunological puzzles.

We focus on establishing causal links between genes and cell states. We apply key concepts from causal learning to go beyond correlations, and generate large-scale perturbational data where possible.

For this we use a systems perspective. Modern perturbational datasets give unique problems for inference and statistics, meaning we push the frontier of deep probabilistic models and amortization. Models are always validated in the lab; a true dream for any aspiring machine learner.

We apply this on urgent problems in immunology, such as immune cell homeostasis, inflammation and oncology.

Areas of Expertise

• Artificial intelligence
• Systems immunology
• Perturbational omics
• Automated analysis of
• Cell-cell communication

Technology Transfer Potential

• Perturbational high-content screening of targets
• AI models for more rapid target discovery
• Efficient omics data generation, balancing investment with statistical power

Selected publications

• Liu W.*, Saelens W.* et al. Dissecting the impact of transcription factor dose on cell reprogramming heterogeneity using scTF-seq. Nature Genetics, 1-14, 2025. Visit ➚
• Guilliams M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell, 185, 379-396, 2022. Visit ➚
• Browaeys R., Saelens W., et al. NicheNet: modeling intercellular communication by linking ligands to target genes. Nature Methods, 17, 159-162, 2020. Visit ➚
• Saelens W.et al. ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning. Nature Communications, 16, 317, 2025. Visit ➚
• Saelens W., et al. A comparison of single-cell trajectory inference methods. Nature Biotechnology, 2019. Visit ➚

Bibliography

• Full bibliography Visit ➚

External links

• Full website Visit ➚