In a cell, the function of most cellular components (genes, proteins, metabolites, micro-RNA, etc.) is brought to bear through the interaction with other cellular components. The interconnectivity among bio-molecules implies that the relation between the entire set of genes in a cell (genotype) and their physical manifestation (phenotype) is extremely complex, since it is mediated by these complex molecular networks. Network medicine is a recent paradigm that exploits the organizing principles of human cellular networks and links network structures to disease.
From a network medicine perspective, hereditary diseases can be seen as perturbations of “disease modules” in the interactome. An important effort in our lab has been aimed at quantifying similarity between hereditable diseases at molecular level by bringing together the existing information that is scattered across the vast corpus of biomedical literature. In other words, we obtain a number that accurately quantifies distance between disease modules in the interactome.
Quantifying disease similarity at molecular level enables the transfer of knowledge between similar diseases, providing hypotheses for causal genes discovery and even suggestions for drug repositioning. This is particularly important for hereditary diseases for which no disease gene is currently known – about 30% of them. For these orphan diseases, our measure can help pinpoint the location of their molecular perturbations. Our measure can also be used for differential diagnosis, aiding medical practitioners in identifying putative alternative diagnosis that are obscured by the complexity and multiplicity of the symptoms. Importantly, we have shown that our measure can be used effectively in the prediction of candidate disease genes.
In our lab research in Network Medicine has been funded by the BBSRC (grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1)