CNORfuzzy-package {CNORfuzzy} | R Documentation |
This package does optimisation of constrained Fuzzy logic networks of signalling pathways based on a previous knowledge network and a set of data collected upon perturbation of some of the nodes in the network.
Package: | CNOR |
Type: | Package |
Version: | 1.4.0 |
Date: | 2013-08-28 |
License: | GPL-2 |
LazyLoad: | yes |
Depends: | R (>= 2.15.0), CellNOptR (>= 1.3.29), nloptr (>= 0.8.5) |
M.K. Morris
Maintainer: T. Cokelaer <cokelaer@ebi.ac.uk>
J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt and P. K. Sorger. Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction, Molecular Systems Biology, 5:331, 2009.
Morris MK, Saez-Rodriguez J, Clarke DC, Sorger PK, Lauffenburger DA (2011). Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli. PLoS Comput Biol. 7(3): e1001099.
CellNOptR package.
# Get data from CellNOptR package data(CNOlistToy,package="CellNOptR") data(ToyModel,package="CellNOptR") # Use the default parameters and set Data and Model paramsList=defaultParametersFuzzy() paramsList$data<-CNOlistToy paramsList$model<-ToyModel ## Not run: # Run the simulator Res = CNORwrapFuzzy(data=CNOlistToy, model=ToyModel, paramsList=paramsList) ## End(Not run)