Dynemo is a powerful, analytical methodology that enables the discovery of accurate and reproducible biomarkers from gene expression studies supporting personalized medicine.
Technology
Cells are exposed to multiple signals that coordinately regulate processes such as cell fate determination, cell movements, differentiation and organogenesis. Signaling pathways are assembled into higher order protein-protein interaction networks (interactomes) that integrate multiple signaling pathways to provide precise cellular outputs, thus alterations in the network structure of interactomes have broader implications on the cell than alterations in individual signaling pathways. Understanding these network structures can provide valuable insights into means of predicting clinical outcomes such as drug responsiveness and disease progression.
The Dynemo algorithm maps gene expression data from biological samples to the interactome database and determines the correlation of co-expression of each interacting protein pair. Using a clinically annotated gene expression dataset, the algorithm reveals modularity signatures in the interactome associated with clinical outcomes. Upon further validation and refinement, these modularity signatures are useful biomarkers supporting personalized medicine.
Technology
Cells are exposed to multiple signals that coordinately regulate processes such as cell fate determination, cell movements, differentiation and organogenesis. Signaling pathways are assembled into higher order protein-protein interaction networks (interactomes) that integrate multiple signaling pathways to provide precise cellular outputs, thus alterations in the network structure of interactomes have broader implications on the cell than alterations in individual signaling pathways. Understanding these network structures can provide valuable insights into means of predicting clinical outcomes such as drug responsiveness and disease progression.
The Dynemo algorithm maps gene expression data from biological samples to the interactome database and determines the correlation of co-expression of each interacting protein pair. Using a clinically annotated gene expression dataset, the algorithm reveals modularity signatures in the interactome associated with clinical outcomes. Upon further validation and refinement, these modularity signatures are useful biomarkers supporting personalized medicine.
