getLBodeContObjFunction: Returns the objective function to perform parameter...
In CNORode: ODE add-on to CellNOptR

Description Usage Arguments Details Value Author(s) See Also Examples

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. This function can be particularly useful if you are planing to couple a nonlinear optimization solver. The returned value of the objective function corresponds to the mean squared value normalized by the number of data points.

	getLBodeContObjFunction(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, lambda_tau=0, lambda_k=0,
	bootstrap=F, SSpenalty_fac=0, SScontrolPenalty_fac=0, boot_seed=sample(1:10000,1))

`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 `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.
`lambda_tau`	Tunable regularisation parameters to penalise L1-norm of parameters tau and induce sparsity. We recommend testing values between 0 and 100 (in log scale) to find best compromise between good fit and sparse model. Default =0, corresponding to no regularisation.
`lambda_k`	Tunable regularisation parameters to penalise L1-norm of parameters k and induce sparsity. We recommend testing values between 0 and 100 (in log scale) to find best compromise between good fit and sparse model. Default =0, corresponding to no regularisation.
`bootstrap`	If set to TRUE performs random sampling with replacement of the measurements used in the optimisation (to be run multiple times to get bootstrapped distribution of parameters). Default =FALSE, no bootstrapping.
`SSpenalty_fac`	Penalty factor for penalising solutions which do not reach steady state. Default =0.
`SScontrolPenalty_fac`	Penalty factor for penalising solutions for which the control (unperturbed) condition (assumed to be first row) does not reach steady state. Default =0.
`boot_seed`	Seed used for random sampling if bootstrap=TRUE. Default chose random seed between 0 and 10000

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 vector containing both continuous parameters and integer values representing which reactions should be kept in the model.

David Henriques, Thomas Cokelaer, Federica Eduati

CellNOptR createLBodeContPars

 	library(CNORode)
	data("ToyCNOlist",package="CNORode");
	data("ToyModel",package="CNORode");
	data("ToyIndices",package="CNORode");
	
	ode_parameters=createLBodeContPars(model,random=TRUE);
	minlp_obj_function=getLBodeContObjFunction(cnolistCNORodeExample, model,ode_parameters,indices);
	
	x=ode_parameters$parValues;
	
	f=minlp_obj_function(x);