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Identifying biological mechanisms of action (e.g. genes, functional elements, or biological pathways) that control disease states, drug response, and altered cellular function is a multifaceted problem involving a dynamic system of biological variables that culminate in an altered cellular state. The challenge is in deciphering the factors that play key roles in determining the cell’s fate. In this talk I will present an overview of various efforts by our group to develop statistical models and methods for identification of cellular mechanisms of action. Common to all of our approaches is the use of certain perturbed Gaussian graphical models, which allows us to formulate the identification problem as a network-based statistical inverse problem. Illustrations will be given in the context of yeast experiments and human cancer.
