# CNORfeeder CNORfeeder is an add-on to [CellNOptR](www.cellnopt.org) 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: F. Eduati, J. De Las Rivas, B. Di Camillo, G. Toffolo, J. Saez-Rodriguez. [Integrating literature-constrained and data-driven inference of signalling networks](http://bioinformatics.oxfordjournals.org/content/28/18/2311). *Bioinformatics*, 2012 , 28 (18) E. Gjerga, P. Trairatphisan, A. Gabor, H. Koch, C. Chevalier, F. Ceccarelli, A. Dugourd, A. Mitsos, J. Saez0Rodriguez. [Converting networks to predictive logic models from perturbation signalling data with CellNOpt](https://www.biorxiv.org/content/10.1101/2020.03.04.976852v1). *biRxiv*, 2020 , 28 (18) ## Requirements All network modelling steps were performed in [R](https://www.r-project.org/) v3.5.1 and visualised using [Cytoscape v3.4.](http://chianti.ucsd.edu/cytoscape-3.4.0/) Other prerequisites include downloading and installing the following `R` package dependencies: ### Bioconductor + [CellNOptR](https://bioconductor.org/packages/release/bioc/html/CellNOptR.html) + [MEIGOR](https://www.bioconductor.org/packages/release/bioc/html/MEIGOR.html) + [CNORode](https://github.com/saezlab/CNORode) ### CRAN + [doParallel](https://cran.r-project.org/web/packages/doParallel/index.html) + [readr](https://cran.r-project.org/web/packages/readr/index.html) + [infotheo](https://cran.r-project.org/web/packages/infotheo/index.html) + [igraph](https://cran.r-project.org/web/packages/igraph/index.html) ## Installation CellNOpt-Feeder is currently available for the installation as an R-package from our GitHub page ```R # Install CARNIVAL from Github using devtools # install.packages('devtools') # in case devtools hasn't been installed library(devtools) install_github('saezlab/CNORfeeder', build_vignettes = TRUE) # or download the source file from GitHub and install from source install.packages('path_to_extracted_CNORfeeder-main_directory', repos = NULL, type="source") ``` ## Toy Example Here we show how Dynamic-Feeder can be applied on a single execution script over a simple toy example ([ToyMMB](http://www.bioconductor.org/packages/release/bioc/html/CellNOptR.html)). Sourcing all the necessary packages ```R library(CellNOptR) library(MEIGOR) library(CNORode) library(doParallel) library(readr) library(infotheo) library(igraph) library(CNORfeeder) ``` Loading the toy example ```R # loading the model data(ToyModel_Gene, package="CNORfeeder") # loading the data data(CNOlistToy_Gene, package="CNORfeeder") # plotting the model and the data plotModel(model = model, CNOlist = cnolist) plotCNOlist(CNOlist = cnolist) ## Loading database data(database, package="CNORfeeder") ``` Setting the initial parameters (here parameters 'k' and 'tau' are optimised and 'n' fixed to 3) and optimization settings for the initial training of the model. ```R ode_parameters=createLBodeContPars(model, LB_n = 1, LB_k = 0, LB_tau = 0, UB_n = 3, UB_k = 1, UB_tau = 1, default_n = 3, default_k = 0.5, default_tau = 0.01, opt_n = FALSE, opt_k = TRUE, opt_tau = TRUE, random = TRUE) ## Parameter Optimization # essm paramsSSm=defaultParametersSSm() paramsSSm$local_solver = "DHC" paramsSSm$maxtime = 60; paramsSSm$maxeval = Inf; paramsSSm$atol=1e-6; paramsSSm$reltol=1e-6; paramsSSm$nan_fac=1000; paramsSSm$dim_refset=30; paramsSSm$n_diverse=1000; paramsSSm$maxStepSize=Inf; paramsSSm$maxNumSteps=10000; paramsSSm$transfer_function = 4; paramsSSm$lambda_tau=0.1 paramsSSm$lambda_k=0.01 paramsSSm$bootstrap=F paramsSSm$SSpenalty_fac=0 paramsSSm$SScontrolPenalty_fac=0 ``` Initial training of the model ```R opt_pars=parEstimationLBode(cnolist, model, method="essm", ode_parameters=ode_parameters, paramsSSm=paramsSSm) simData = plotLBodeFitness(cnolist = cnolist, model = model, ode_parameters = opt_pars, transfer_function = 4) ``` Identifying the mis-fits (measurements with mse worse than 0.05) and interactions from the database which we want to integrate (on this case only through data-driven method) ```R ## Identifying which measured nodes at which experimental condition have a worse ## fit in terms of mse value compared to the specified threshold mseThresh value indices = identifyMisfitIndices(cnolist = cnolist, model = model, simData = simData, mseThresh = 0.05) ## Alternatively, users can bypass the initial training of the model and add ## new links through the FEED or database search regardless how well some ## measurements were already fitted simply by setting simData=NULL # indices = identifyMisfitIndices(cnolist = cnolist, model = model, simData = NULL) ## Obtaining the object indicating which measurements at which condition are ## poorly fitted feederObject = buildFeederObjectDynamic(model = model, cnolist = cnolist, indices = indices, database = NULL, DDN = TRUE) ## Integration of new links integratedModel = integrateLinks(feederObject = feederObject, cnolist = cnolist) ``` Plotting the integrated model by highlighting in purple the new added links to the PKN ```R plotModel(model = integratedModel$model, CNOlist = cnolist, indexIntegr = integratedModel$integLinksIdx) ``` Setting the initial ODE parameters to optimize for the integrated model ```R ode_parameters=createLBodeContPars(integratedModel$model, LB_n = 1, LB_k = 0, LB_tau = 0, UB_n = 3, UB_k = 1, UB_tau = 1, default_n = 3, default_k = 0.5, default_tau = 0.01, opt_n = FALSE, opt_k = TRUE, opt_tau = TRUE, random = TRUE) ``` Optimizing the integrated model with low penalty factor (lambda = 2) of the integrated links - here we observe the effects of the new links on the improvements in the fitting quality ```R res1 = runDynamicFeeder(cnolist = cnolist, integratedModel = integratedModel, ode_parameters = ode_parameters, paramsSSm = paramsSSm, penFactor_k = 2) plotLBodeFitness(cnolist = res1$CNOList, model = res1$`Integrated-Model`$model, ode_parameters = res1$Parameters, transfer_function = 4) ``` Optimizing the integrated model with high penalty factor (lambda_k = 10000) of the integrated links - here we do not observe any effects of the new links on the improvements in the fitting quality ```R res2 = runDynamicFeeder(cnolist = cnolist, integratedModel = integratedModel, ode_parameters = ode_parameters, penFactor_k = 10000, paramsSSm = paramsSSm) plotLBodeFitness(cnolist = res2$CNOList, model = res2$`Integrated-Model`$model, ode_parameters = res2$Parameters, transfer_function = 4) ```