  Cellular response to a perturbation is the result of a dynamic system of
biological variables linked in a complex network. A major challenge in drug and
disease studies is identifying the key factors of a biological network that are
essential in determining the cell's fate.
  Here our goal is the identification of perturbed pathways from
high-throughput gene expression data. We develop a three-level hierarchical
model, where (i) the first level captures the relationship between gene
expression and biological pathways using confirmatory factor analysis, (ii) the
second level models the behavior within an underlying network of pathways
induced by an unknown perturbation using a conditional autoregressive model,
and (iii) the third level is a spike-and-slab prior on the perturbations. We
then identify perturbations through posterior-based variable selection.
  We illustrate our approach using gene transcription drug perturbation
profiles from the DREAM7 drug sensitivity predication challenge data set. Our
proposed method identified regulatory pathways that are known to play a
causative role and that were not readily resolved using gene set enrichment
analysis or exploratory factor models. Simulation results are presented
assessing the performance of this model relative to a network-free variant and
its robustness to inaccuracies in biological databases.
