Comparing Signaling Networks between Normal and Transformed Hepatocytes Using Discrete Logical Models

Abstract

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.

Models of signalling networks – what cell biologists can gain from them and give to them

“…Discrete logical models have recently emerged as tools for cell signalling. Saez-Rodriguez and co-workers built multiple models of hepatocyte signalling in response to stimuli and signalling inhibitors Saez-Rodriguez et al, 2011). Starting with a comprehensive literature-curated network, the authors refined the wiring to capture measured patterns of immediate-early signalling.…”

Systems biology of virus-host signaling network interactions

“…By integrating their data using a Boolean-based model, they confirmed the main targets of four drugs as well as uncovered several unknown off-target effects, demonstrating an efficient way to determine drug selectivity and potency. Similarly, a Boolean model of growth factor receptor activities in response to different ligands or drugs discovered a poorly documented off target effect of TPCA-1, an IκB inhibitor (33). Similar approaches may demonstrate promising applications to investigate anti-viral drug actions.…”

Logic Modeling in Quantitative Systems Pharmacology

“…This information is important to highlight the specific mechanisms of regulation in different contexts. For example, comparing signaling pathways of healthy and diseased cells allows investigating which mechanisms are deregulated as a cause or an effect of the disease 16, 17. Similarly, the comparison of pathway activity in different cells from the same cancer type allows investigating mechanism of resistance to drugs, which can be exploited to suggest personalized therapies 18, 19, 20.…”

“…The available data and publications related to the chosen system are usually a starting point for the following steps 41. In other cases, new experiments are designed and carried out to provide the necessary data for modeling 16. For this tutorial, we decided to study a small signaling network in prostate cancer, and do so using published data describing the phosphorylation response of key proteins in prostate cancer cell lines in response to the addition of several ligands and inhibitors 57…”