Model-based pathway enrichment analysis applied to the TGF-beta regulation of autophagy in autism.

To differentiate between conditions of health and disease, current pathway enrichment analysis methods detect the differential expression of distinct biological pathways. System-level model-driven approaches, however, are lacking. Here we present a new methodology that uses a dynamic model to suggest a unified subsystem to better differentiate between diseased and healthy conditions. Our methodology includes the following steps: 1) detecting connections between relevant differentially expressed pathways; 2) construction of a unified in silico model, a stochastic Petri net model that links these distinct pathways; 3) model execution to predict subsystem activation; and 4) enrichment analysis of the predicted subsystem. We apply our approach to the TGF-beta regulation of the autophagy system implicated in autism. Our model was constructed manually, based on the literature, to predict, using model simulation, the TGF-beta-to-autophagy active subsystem and downstream gene expression changes associated with TGF-beta, which go beyond the individual findings derived from literature. We evaluated the in silico predicted subsystem and found it to be co-expressed in the normative whole blood human gene expression data. Finally, we show our subsystem's gene set to be significantly differentially expressed in two independent datasets of blood samples of ASD (autistic spectrum disorders) individuals as opposed to controls. Our study demonstrates that dynamic pathway unification can define a new refined subsystem that can significantly differentiate between disease conditions.