Computational Autoimmunity

On a daily basis, we are exposed to a plethora of organisms, some of which could result in disease. Keeping these agents at bay is the human immune system. Through evolution the immune system has developed into a formidable defense system capable of efficiently ridding our body of disease causing agents without harming us in the proces. The capability of the immune system to distinguish what is our own body (self) from what is not (non-self) is called central tolerance. However, some diseases like e.g. Type 1 diabetes mellitus, Psoriatic arthritis, a.o. arise when our immune system breaks this central tolerance and starts targeting our own body. This is known as autoimmunity.

In the Computational Autoimmunity group, we work on increasing our understanding of what happens when central tolerance is broken, how the specificity of our immune system defines self and non-self, aiming at elucidating the immuno-cellular mechanisms influencing autoimmune disease. Furthermore, a key element of the group activities is the development and application of computational technology aiming at solving bioinformatics challenges, where we have a strong emphasis on machine learning. Our primary goal for research-to-application is to activate this knowledge to identify specific, clinically relevant and practically accessible biomarkers with predictive power to stratify patients to optimise response-to-treatment.

A key approach to interrogating the immuno-cellular mechanisms influencing autoimmune disease is single-cell analysis. Single-cell analysis has transformed the resolution level for understanding complex cellular interactions. The field is rapidly evolving and in the Computational Autoimmunity group, we actively work to engage the international community through outreach and interaction with the innovation culture and ongoing collaborative efforts.

We are always open for collaborations, so do not hesitate to contact us for more information.