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A Computational Systems Biology Approach to Reveal the Molecular Basis of Complex Diseases

Final Report Summary - EYLCOMPDISSYSBIO (A Computational Systems Biology Approach to Reveal the Molecular Basis of Complex Diseases)

Complex human diseases such as Parkinson disease, diabetes and cancer, are caused by multiple genetic factors and despite significant efforts often remain incurable. A comprehensive understanding of the molecular mechanisms underlying complex diseases is essential for opening new avenues for treatment. In an effort to reach this understanding, complex diseases are increasingly being studied using state-of-the-art high-throughput assays that offer unprecedented views into their genomic and transcritpomic features. The goal of this project was to develop computational approaches that meaningfully integrate these data to identify the cellular pathways underlying diseases.
A main approach that we exploited is ResponseNet, a network optimization framework that we developed previously and applied successfully to analyze data of a yeast disease model (Yeger-Lotem et al, Nature Genetics 2009). In this project we extended ResponseNet and tailored it to analyze human data. Specifically, ResponseNet can now be used to identify high-probability pathways in the human molecular interaction network that connect genes associated with a specific condition to genes that are differentially expressed in that condition. In its current form ResponseNet can be used to identify signaling pathways as before, but also regulatory pathways that include regulation by micro-RNAs, and protein-protein interaction pathways. We assessed the performance of ResponseNet in-silico by using manually-curated molecular interaction pathways in humans. To enable wide usage of ResponseNet by the scientific community we implemented it as a freely available web-server (netbio.bgu.ac.il/respnet). The web-server and assessments were published (Basha et al, Nucleic Acids Research 2013). We applied ResponseNet to reveal the pathways that underlie melanoma disease, by predicting the sub-network connecting melanoma-associated mutations and transcripts that were differentially expressed in melanoma cell lines; and the signaling pathway connecting two key anti-inflammatory proteins: α-1-antitripsin and IL-1 receptor antagonist. For melanoma we have promising results in-silico. For the anti-inflamatory proteins we experimentally validated the involvement of RELA, a subunit of the NF-kappa-B transcription factor complex, in the pathway. A second approach that we developed is a context-sensitive network model, in which genes and protein nodes are assigned multiple contexts based on their gene ontology annotations, and their interactions are associated with multiple context-sensitive scores. Using this model, we created an algorithm and a corresponding tool, ContextNet, based on a dynamic programming, for finding high-ranking context-sensitive paths in networks (Lan et al, ISMB 2013 and Bioinformatics). A third approach that we developed recently is a tissue-based analysis of human pathways. We integrated data of interactions and tissue expression profiles to construct extensive protein-interaction networks for 16 tissues (Barshir et al, Nucleic Acids Research 2013). We then developed comparative analysis of tissue networks, and showed their value in highlighting mechanisms that underlie hereditary diseases (Barshir et al, PLoS Computational Biology 2014). The studies in tools from my lab are accessible through my lab website at netbio.bgu.ac.il
The IRG funding helped me establish a productive and successful lab at Ben-Gurion University. We published several papers in leading journals in the field and won competitive grants. Mostly, the funding gave me the opportunity to push forward the usage of network biology to enhance our understanding of human disease pathways, and hopefully to open new avenues for therapy.