Biological network models have become standard tools for genome-wide analysis of both cancer disease processes and healthy differentiation from stem cells. In this thesis, I ad- dress a method for evaluating network models in terms of their ability to predict held out expression data given information about other genes in the same network. I apply this test to several extensions of our pathway database to demonstrate the transcrip- tional modeling utility of reverse phase protein array data, natural language processed literature, and transcription factor target predictions in stem cells and differentiated tissue. I then explore the addition of one new type of network data; co-localization in DNA domains. However, preexisting functional data is not the only source of networks. In the final part of the thesis, I elucidate a method to integrate prior biological knowl- edge with time series observations to infer causal relationships between phosphorylation events on proteins.