Abstract

Pathway maps are useful abstractions of signaling networks but have two key limitations: they are not computable models that can be compared to functional data, and they are not cell-specific, a significant limitation because it is precisely biochemical differences between normal and diseased cells that are targeted for pharmaceutical intervention.

We have recently developed an efficient method to construct predictive logic models of signaling networks based on generic pathway maps and high-throughput functional data (Saez-Rodriguez et al., Mol. Sys. Biol., 5:331, 2009). The method is embedded in the toolbox CellNetOptimizer that works in concert with DataRail (Saez-Rodriguez et al, Bioinformatics, 2008), a complementary toolbox for managing and transforming varied data.

We apply the method to distinguishing the topologies of immediate early signaling networks in primary human hepatocytes and four hepatocellular carcinoma (HCC) cell lines. We show that five distinct models cluster topologically into normal and diseased sets, revealing functional differences between normal and diseased cells that involve activation of growth factor receptors and intracellular kinase cascades. In a proof-of-principle experiment we also infer a target for an inhibitor developed to treat arthritis and airway inflammation.

Hosted by Florian Markowetz.