%0 Journal Article
%T CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms
%A Camille D A Terfve
%A Thomas Cokelaer
%A David Henriques
%A Aidan MacNamara
%A Emanuel Gon£żalves
%A Melody K Morris
%A Martijn van Iersel
%A Douglas A Lauffenburger
%A Julio Saez-Rodriguez
%J BMC Systems Biology
%D 2012
%I BioMed Central
%R 10.1186/1752-0509-6-133
%X Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities.Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.
%U http://www.biomedcentral.com/1752-0509/6/133/abstract