Logic Network Modelling of cancer signalling pathways

Abstract

The purpose of the project is to reconstruct Boolean models of signaling. Conventionally, large-scale protein-protein interactions were viewed as static models. Recently functional models of these networks have been suggested ranging from Boolean to constraint-based models. Most of these models rely on extensive human curation thereby making it difficult to learn these models from large data sets. The primary intention of the paper is to infer Boolean models of signaling, automatically from data. The approach is applied to growth and inflammatory signaling systems in human and show how the learning phase can improve the fit of the model to experimental data, remove spurious interactions and lead to better understanding of the system at hand.