Network topology as a source of information
published: Dec. 3, 2012, recorded: November 2012, views: 5129
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !