Human diseases are inherently complex and involve a multitude of interconnected proteins, which function in groups defined as pathways and complexes. In order to understand disease pathology, one has to assemble an “inventory” of molecular objects involved and reconstruct interactions between them. We aim to create a map (causal model) of LRRK2 pathways in Parkinson’s disease. This will help to reconstruct disease pathology mechanisms and facilitate drug discovery and novel biomarkers for Parkinson’s disease.
The project will be carried out by a team of disease biologists and annotators at a Thomson Reuters facility using proprietary annotation tools. The model will consist of “nodes” – proteins, genes, compounds etc., connected via “edges” – protein interactions. The data supporting interactions will be picked up from Thomson Reuters knowledge base MetaBase and from public literature. The resulted map (causal model) will be considered within the context of hundreds of other disease and normal pathway maps and manually annotated biomarkers for Parkinson’s disease.
Relevance to Diagnosis/Treatment of Parkinson’s Disease:
After decades of “reductionist” methodology to drug discovery, it is now accepted that fundamental understanding of disease biology is necessary for rational molecular diagnostics, disease treatment and prevention. Our map (causal model) is the first step in building a rational Parkinson’s disease analysis platform primarily designed for disease researchers working on drug discovery and diagnostics.
As the outcome, we will deliver at least one pathway map (causal model) describing the role of LRRK2 pathway map for Parkinson’s disease pathology. The high resolution map will be visualized in MetaCore data analysis package and also available as an XML file. In addition to the image, the map will have an overview in a form of a text file with references.