Date(s) - 29/01/2013
2:00 pm - 3:00 pm
SGDP seminar room
Title: Network Topology Complements Sequence as a Source of Biological Information
Sequence-based computational approaches have revolutionized biological understanding. However, they can fail to explain some biological phenomena. Since proteins aggregate to perform a function, the connectivity of a protein-protein interaction (PPI) network will provide additional insight into the inner working on the cell. We argue that sequence and network topology give insights into complementary slices of biological information, which sometimes corroborate each other, but sometimes do not. Hence, the advancement depends on the development of sophisticated graph-theoretic methods for extracting biological knowledge purely from network topology before being integrated with other types of biological data (e.g., sequence). However, dealing with large networks is non-trivial, since many graph-theoretic problems are computationally intractable, so heuristic algorithms are sought.
Analogous to sequence alignments, alignments of biological networks will likely impact biomedical understanding. We introduce a family of topology-based network alignment algorithms, that we call GRAAL algorithms, which produces by far the most complete alignments of biological networks to date. Also, we demonstrate that topology around cancer and non-cancer genes is different and when integrated with functional genomics data, it successfully predicts new cancer genes in melanogenesis-related pathways. Finally, we find that aging, cancer, pathogen-interacting, drug-target and genes involved in signaling pathways are topologically “central” in the network, occupying dense network regions and “dominating” other genes in the network. We conclude that network topology is a valuable source of biological information that can suggest novel drug targets and impact therapeutics.