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Network topology as a source of information

Network topology as a source of information

This video was recorded at Solomon seminar. Many real-world phenomena can be represented as networks of interconnected entities. For example, individual genes are just a means to an end: they produce proteins that interact in complex networked ways and make our cells work. Hence, using protein interaction networks (PINs) to predict protein function and involvement in disease has received much attention in the post-genomic era. We develop novel measures of network topology to predict function of unannotated proteins in the human PIN. We find that human genes involved in key biological processes and pathways, such as aging, cancer, infectious diseases, signaling and drug-targeted pathways, occupy regions of the network that correspond to its "spine" that connects all other network parts and can thus pass cellular signals fast throughout the network. We design methods that harvest information from network topology and gain new biological information, such as suggest novel drug targets for therapeutic intervention. For example, our network-based predictions of novel proteins that participate in melanogenesis in human cells are phenotypically validated.

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