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Kinases are an integral part of cellular signaling circuitry. In recent years, we have uncovered a wealth of evidence linking individual kinases to particular conditions and have identified how inhibition of certain kinases can lead to therapeutic benefits.The majority of this research has focused on recombinant kinases in vitro. However, kinases do not act alone in vivo; They act as a network with complex interdependencies and multiple pathways from one kinase in the network to the other. The availability of microarray based methods like the Pamgene PamChip\reg has allowed for activity profiling of multiple kinases simultaneously. This data is valuable for understanding how a system of kinases interacts in the presence of other kinases.In this thesis we propose a method to deconvolve and construct a kinase interaction network from the PamChip\reg Assay output using Bayesian Network Modelling. This approach allows us to construct the networks using the output from PamChip\reg Assay. This approach further allows us to assign specific upstream kinases to each substrate present on the chip. This approach was then used to analyze three datasets for Alzheimer's disease. We uncovered the important role of Protein Kinase A (PRKACA) in signaling in Alzheimer's Disease. We also validated our networks against literature and discovered them to be fairly accurate of the current state of research.The ability to deconvolve and construct such networks from the PamChip Assays allows us to visualize these networks in particular conditions and compare networks across conditions to see how a network changes in response to disease. The same technique may then be applied to drug treated cells to identify potential treatments that \emph{reverse} a diseased network to its \emph{reference} state.