COMPARING SIGNALING NETWORKS BETWEEN NORMAL AND TRANSFORMED HEPATOCYTES USING DISCRETE LOGICAL MODELS

摘要

Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks based on 'omic data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks, but are rarely context-specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here we combine network analysis and functional experimentation using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and four hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type that clustered topologically into normal and diseased sets. Comparison revealed that clustering arises from systematic differences in signaling logic in three regions of the network. We also infer the existence of a new interaction involving Jak-Stat and NFκB signaling and show that it arises from the polypharmacology of an IκB kinase inhibitor rather than previously unidentified protein-protein associations. These results constitute a proof-of-principle that receptor-mediated signal transduction can be reverse engineered using biochemical data so that the immediate effects of drugs on normal and diseased cells can be studied in a systematic manner.