  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 differential dependency networks that incorporates
both conditional data and prior knowledge as a convex optimization problem, and
developed an efficient learning algorithm to jointly infer the conserved
biological network and the significant rewiring across different conditions. We
used a novel sampling scheme to estimate the expected error rate due to random
knowledge and based on which, developed a strategy that fully exploits the
benefit of this data-knowledge integrated approach. We demonstrated and
validated the principle and performance of our method using synthetic datasets.
We then applied our method to yeast cell line and breast cancer microarray data
and obtained biologically plausible results.
