Before revision: Among network modeling tasks, identifying the rewiring of network structure is particularly instrumental in revealing and pinpointing the molecular cause of a disease. Effective incorporation of biological prior knowledge into network learning algorithms can leverage domain knowledge and make data driven inference more robust and biologically relevant. We formulate the inference of condition specific network structures that incorporates relevant prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the biological networks as well as their changes.
After revision: Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of condition specific network structures that incorporates relevant prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the biological networks as well as their changes.