Comprehensive understanding of the molecular mechanisms that underlie incurable complex diseases is essential for opening new avenues for treatment. In an effort to elucidate their mechanisms, complex diseases are increasingly studied using state-of-the-art high-throughput assays, including genome-wide association studies (GWAS) and mRNA profiling. However, each assay enables only a limited understanding of disease processes. GWAS typically identify many genomic loci whose relation to the disease was previously unknown, but do not reveal the loci’s mode of action. Likewise, mRNA profiling identify transcriptional changes that occur in disease, but do not reveal the cellular pathways leading to them. Integrative analysis of these valuable data has a great potential to reveal a much broader view of disease processes. I propose a novel network-based framework that infers disease pathways by relating the results of GWAS and mRNA profiling assays through known molecular interactions. Application of this approach to data of Parkinson disease will provide a novel functional view into the disease processes and facilitate the generation of hypotheses, which will be tested in silico and in vitro in collaboration with disease experts. The project aims are: 1. Create a probabilistic network model of the human interactome. 2. Develop network-optimization algorithms to distill and integrate disease data. 3. Identify new cellular pathways related to Parkinson disease. 4. Validate experimentally these new findings. This line of research became feasible owing to recent accumulation of large-scale disease data and relies on my extensive research experience in network biology. The computational framework may be applied to other complex diseases and can serve as a basis for fruitful collaborations with disease experts and pharmaceutical companies. The IRG award will help me obtain a permanent position at Ben Gurion University and will facilitate knowledge transfer to the EU.
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