getLBodeMINLPObjFunction: Get the objective function to evaluate the fitness of a given model structure and set of parameters.

Description

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.

Usage

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)

Arguments

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 createLBodeContPars.

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.

Value

Returns a function to evaluate the model fitness. This function receives a continuous parameter vector.

Details

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.

Examples

Run this code

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);