Cellular responses to external stimuli depend on dynamic features of multi-pathway network signaling; thus, the behavior is of a cell is influenced in a complex manner by its environment and by intrinsic properties. Methods of multi-variate systems analysis have provided an understanding of these convoluted effects, but to date this has been only for relatively simplified in vitro cell culture examples. An unaddressed question is whether such approaches can be successfully brought to bear on in vivo conditions. We have analyzed the in vivo signaling network that determines the response of intestinal epithelial cells to the pro-inflammatory cytokine tumor necrosis factor α (TNF-α). We built data-driven, partial least-squares discriminant analysis (PLSDA) models based on signaling, apoptotic, and proliferative responses in the mouse small intestinal epithelium after systemic exposure to TNF-α. We identified the extracellular signal–regulated kinase (ERK) signaling axis as a critical modulator of the temporal variation in apoptosis at different doses of TNF-α, as well as the spatial variation in proliferative responses in distinct intestinal regions. Pharmacologic inhibition of MEK, a mitogen-activated protein kinase kinase upstream of ERK, integrally altered the signaling network and changed the temporal and spatial phenotypes in accordance with a priori model predictions. Our results demonstrate the dynamic, adaptive nature of in vivo signaling networks and identify natural, tissue-level variation in response phenotypes that can be deconvoluted only with quantitative, multi-variate computational modeling. To our knowledge, this is the first study to apply computational modeling of signaling networks to a bona fide in vivo system, and it lays a foundation for the use of systems-based approaches to understand how dysregulation of the cellular network state underlies complex disease.

Drs An, Huang, and Wan contributed equally and are joint first authors.