Many projects in the group focus on the construction or analysis of networks. This includes being involved in the development of very popular network databases, such as STRING, are is detailed on the network resources page and covered in our training material. In addition to resource development, we have a number of research projects that use network biology to analyze human diseases.
The study of host–parasite interactions is essential to understand parasitic infection and adaptation within the host system as well as to prevent and treat infectious diseases. Our group works on several projects that aim to integrate data on protein–protein interactions from existing databases, complement them with knowledge that is automatically text-mined from the literature, and use this to predict molecular interactions between host and pathogen proteins. The projects cover viruses (STRING Viruses), uni- and multi-cellular eukaryotic pathogens (OrthoHPI), and most recently bacteria.
Animal models of diseases
Having a good animal model of a disease is critical to study its molecular basis and to test possible treatments. However, it can be difficult to tell model organisms are best suited to study a given disease. We collaborate closely with the the group of Jan Gorodkin to address this challenge as part of the AniGen project. By comparing disease-relevant interaction networks and pathways in the context both evolutionary information and tissue expression data, we aim to identify the differences that make one organism a better animal model of a human disease than another organism.
Network-based CRISPR design
CRISPR technology has revolutionized the gene functions are studied in basic and applied research. The two major challenges in designing CRISPR experiments is to create gRNAs that make the desired edits while avoid unintended edits elsewhere in the genome, and that the functions of the target gene may be compensated for by other genes. In a new project funded by the Novo Nordisk Foundation, we collaborate with the groups of Yonglun Luo and Jan Gorodkin to address both of these challenges, with our group specifically using deep learning on gene networks to address redundancy.
In addition to working with molecular networks in a disease contexts, we collaborate with the Brunak group on construction of networks that capture the relationships between diseases themselves. As partner on an NNF Challenge grant and member of the GALAXY consortium, we apply this to the study of diabetes and alcoholic liver disease, respectively. In both projects, we use the disease networks derived from registry data as a scaffold for other data, including molecular data, with the goal to stratify patients, improve early diagnosis of disease, and gain insights into the transition from healthy to sick.