getLBodeMINLPObjFunction {CNORode} | R Documentation |
This function configures returns the objective function that can be used to evaluate the fitness of a logic based ODE model using a particular set of parameters and model structure. This function can be particular useful if you are planing to couple a mixed integer nonlinear programming optimization solver. The returned value of the objective function corresponds to the mean squared value.
getLBodeMINLPObjFunction(cnolist, model, ode_parameters, indices=NULL, time = 1, verbose = 0, transfer_function = 3, reltol = 1e-04, atol = 0.001, maxStepSize = Inf, maxNumSteps = 1e+05, maxErrTestsFails = 50, nan_fac = 1)
cnolist |
A list containing the experimental design and data. |
model |
The logic model to be simulated. |
ode_parameters |
A list with the ODEs parameter information. Obtained with |
indices |
Indices to map data in the model. Obtained with indexFinder function from CellNOptR. |
time |
An integer with the index of the time point to start the simulation. Default is 1. |
verbose |
A logical value that triggers a set of comments. |
transfer_function |
The type of used transfer. Use 1 for no transfer function, 2 for Hill function and 3 for normalized Hill function. |
reltol |
Relative Tolerance for numerical integration. |
atol |
Absolute tolerance for numerical integration. |
maxStepSize |
The maximum step size allowed to ODE solver. |
maxNumSteps |
The maximum number of internal steps between two points being sampled before the solver fails. |
maxErrTestsFails |
Specifies the maximum number of error test failures permitted in attempting one step. |
nan_fac |
A penalty for each data point the model is not able to simulate. We recommend higher than 0 and smaller that 1. |
Check CellNOptR
for details about the cnolist and the model format.
For more details in the configuration of the ODE solver check the CVODES manual.
Returns a function to evaluate the model fitness. This function receives a continuous parameter vector.
David Henriques, Thomas Cokelaer
library(CNORode) data("ToyCNOlist",package="CNORode"); data("ToyModel",package="CNORode"); data("ToyIndices",package="CNORode"); ode_parameters=createLBodeContPars(model,random=TRUE); minlp_obj_function=getLBodeMINLPObjFunction(cnolistCNORodeExample, model,ode_parameters,indices); n_int_vars=dim(model$interMat)[2]; x_int=round(runif(n_int_vars)) x_cont=ode_parameters$parValues; x=c(x_cont,x_int); f=minlp_obj_function(x);