The Application of Network Models to Therapeutic Target Prediction

Pharmaceutical corporations are very keen in drug target identification, and there has been a great deal of study done on the subject. Both the biological network community and the graph algorithms community are involved in this interdisciplinary research. Synthesizing or inferring the complicated network of interactions related to the disease, linking this network to disease-specific behavior, and predicting which components are key mediators of the behavior are all key phases in a typical therapeutic target identification challenge. All of these phases have to do with graph theory or graph algorithms. We give modeling and algorithmic approaches for therapeutic target identification in this perspective, as well as highlight a number of algorithmic advancements that have received relatively little attention to far, in the hopes of improving the linkages between these two research areas