CellNOpt (from CellNetOptimizer; a.k.a. CNO) is a software used for creating logic-based models of signal transduction networks using different logic formalisms (Boolean, Fuzzy, or differential equations). CellNOpt uses information on signaling pathways encoded as a Prior Knowledge Network, and trains it against high-throughput biochemical data to create cell-specific models.
CellNOpt is freely available under GPL license in R and Matlab languages. It can be also accessed through a python wrapper, and a Cytoscape plugin called CytoCopter provides a graphical user interface.
CellNOpt is mainly developed at the Saez-Rodriguez group at the European Bioinformatics Institute (EBI). The project started at the groups of Peter Sorger (Harvard Medical School) and Doug Lauffenburger (M.I.T.), that continue being involved. There is a group of CellNOpt developers at different locations.
CellNOpt is described in details in the following paper (more literature related to CellNOpt is available in the Publications sections). Please use this reference to cite CellNOpt
C Terfve, T Cokelaer, A MacNamara, D Henriques, E Goncalves, MK Morris, M van Iersel, DA Lauffenburger, J Saez-Rodriguez.
CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.
BMC Systems Biology, 2012, 6:133 PDF
CellNOptR (R Packages)
CellNOptR contains the core functions as well as the boolean and steady states version. It implements the workflow described in Saez-Rodriguez et al Mol Sys Bio 2009, with extended capabilities for multiple time points.
CNORdt is an extension that allows to train a Boolean model agains time-courses of data.
CNORfuzzy is an extension to CellNOptR that allows to handle continous values, using constrained fuzzy logic, as described in Morris et al Plos Comp Bio 2011.
CNORfeeder is an add-on to CellNOptR that permits to extend a network derived from literature with links derived in a strictly data-driven way and supported by protein-protein interactions as described in (Eduati et al Bioinformatics 2012).
Some features of CellNOpt are also available as a MATLAB toolbox, along with the toolbox Q2LM to analyze models, here
Cytoscape Plugin (CytoCopter)
CytoCopteR is a Graphical User Interface designed as a Cytoscape plugin. It provides an interface to CellNOptR using Rserve. More information is available on CytoCopter page.
MEIGO, a global optimization toolbox that includes a number of metaheuristic methods as well as a Bayesian inference method for parameter estimation, that can be applied to model training in CellNOpt. Available in R, Matlab, and Python. Presented in Egea et al BMC Bioinformatics, 214 .
Manual and Tutorial of the R packages
The R packages are self documented. Tutorials and manual are provided on the bioconductor site of each package. Here below are direct links to the Bioconductor vignettes:CNODocs. Besides, the following link provides a tutorial given at In Silico Systems Biology, 2013. The following link provides also a CytoCopteR tutorial.
Published Model and Data Sets
Some model and data sets are provided in the R packages. However, we also provide a more exhaustive set of published models and data files in Model and Data documentation.
If you'd like another model/data set to be added to this page, please write us at .
User mailing list
To be up to date, please join the cno-users mailing list. This low-traffic mailing list is a place to discuss and help each other to install and use CellNOpt software, and to be aware of major developments and new releases.