Graph Theory Based Hypotheses with Experimental Validation to Identify Novel Functions of Components and Pathways in Mammalian Cells

Avi Ma’ayan (Mount Sinai School of Medicine)

Abstract

Mammalian cell signaling and protein interaction networks created from literature or high-throughput experiments can be integrated and analyzed using graph theory. Topology analysis of such networks in combination with advanced experimental techniques that measure many cellular components at once can be used to make predictions about gaps in our current knowledge to predict undiscovered functional roles for components and pathways. This approach has been successful for quality assessment of pre-synaptic proteomics data; it enabled the discovery of new pathways and components in cannabinoid induced neurite outgrowth using transcription-factor arrays in combination with pharmacological inhibitors and RNAi; and assisted in identifying a novel Noonan Syndrome causing gene. Combining biological networks and graph-theory with multivariate experiments can rapidly enhance our understanding of the detailed workings of mammalian cells.

Back to The First q-bio Conference.