minlpLBodeSSm: Search for the best combination of continuous parameters and logic gates.

Description

This function uses essR to search for the best set of continuous parameters and model structure. The objective function is the same as the one provided by getLBodeMINLPObjFunction.

Usage

minlpLBodeSSm(cnolist, model, ode_parameters = NULL, int_x0=NULL, indices = NULL, maxeval = Inf,
	maxtime = 100, ndiverse = NULL, dim_refset = NULL, local_solver = 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.

int_x0

Vector with initial solution for integer parameters.

indices

Indices to map data in the model. Obtained with indexFinder function from CellNOptR.

maxeval

Maximum number of evaluation in the optimization procedure.

maxtime

Maximum number of evaluation spent in optimization procedure.

ndiverse

Duration of the optinisation procedure.

dim_refset

Number of diverse initial solutions.

local_solver

Local solver to be used in SSm.

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 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

LB_n: A numeric value to be used as lower bound for all parameters of type n.
LB_k: A numeric value to be used as lower bound for all parameters of type k.
LB_tau: A numeric value to be used as lower bound for all parameters of type tau.
UB_n: A numeric value to be used as upper bound for all parameters of type n.
UB_k: A numeric value to be used as upper bound for all parameters of type k.
UB_tau: A numeric value to be used as upper bound for all parameters of type tau.
default_n: The default parameter to be used for every parameter of type n.
default_k: The default parameter to be used for every parameter of type k.
default_tau: The default parameter to be used for every parameter of type tau.
LB_in: An array with the the same length as ode_parameters$parValues with lower bounds for each specific parameter.
UB_in: An array with the the same length as ode_parameters$parValues with upper bounds for each specific parameter.
opt_n: Add all parameter n to the index of parameters to be fitted.
opt_k: Add all parameter k to the index of parameters to be fitted.
opt_tau: Add all parameter tau to the index of parameters to be fitted.
random: A logical value that determines that a random solution is for the parameters to be optimised.
model: The best fitting found model structure.
smm_results: A list containing the information provided by the nonlinear optimization solver.

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


## Not run: 
# data("ToyCNOlist",package="CNORode");
# data("ToyModel",package="CNORode");
# data("ToyIndices",package="CNORode");
# 	
# ode_parameters=createLBodeContPars(model,random=TRUE);
# 
# #Visualize initial solution
# simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,ode_parameters,indices=indices)
# ode_parameters=minlpLBodeSSm(cnolistCNORodeExample, model,ode_parameters);
# 
# model=ode_parameters$model;
# 
# #Visualize fitted solution
# simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,indices=indices);
# ## End(Not run)