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# Files description
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### BEFORE RUNNING THE TOOLBOX, THE USER MUST ADD TO THE PATH THE FOLDER CLASSES AND THE FOLDER EXAMPLES WHERE THE MODELS ARE SAVED ###
This is the main code folder for running the CRA Toolbox without an interface and with a model defined as a MATLAB functions. It contains the following files:
1. main_PulseGeneratorNetwork.m is the script for running the CRA Toolbox on the model PulseGeneratorNetwork (path: Examples/PulseGeneratorNetwork.m)
2. main_modelPeng2016.m is the script for running the CRA Toolbox on the Prostate-specific P ten −/− mouse model (path: Examples/model_Peng2016_reduced.m)
3. main_PulseGeneratorNetwork.m is the script for running the CRA Toolbox on the EGFR-IGF1R pathway (path: Examples/EGFR_IGF1R_model.m)
In order to run his own model, the user needs to create a main Matlab script where all the parameters of the procedure and the name of the model are specified.
5. start_simulation.m is the function for the ODE model simulation. It takes in input all the features of the model, the number of independent realizations to
perform and the parameters for creating the Latin Hypercube: the number of samples Ns and the upper and lower boundaries LBpi and UBpi.
For each independent realization, this function generates the Latin Hypercube and simulates the model.
6. compute_MIRI.m is the function executed for calculating the MIRI of each parameter. It takes in input the features of the model, the name of the output
variable of interest, the evaluation function and the method for computing the upper and lower tails of the parameter pdfs, the number of samples to include
in each tail of the parameter pdf, all the simulation results and the folder where the results and the images are saved.
It plots and saves boxplot of MIRI values and the pdf of the evaluation function of the chosen variable. It saves MIRI values and the pdfs of both the
evaluation function and parameters of the model. It also generates the histogram of the evaluation function.
Currently, it is possible to choose among three evaluation functions: area under the curve, maximum value and time
of maximum of the time behavior of the chosen variable.
Currently, it is possible to choose between two methods for computing upper and lower tails of the parameter pdfs: sort and tmp_sum.
The sort method orders all the samples of the evaluation function and selects the N lowest and highest values.
The tmp_sum method calculates the lower and upper quartile of the pdf, computing the threshold values in an adaptive manner.
The initial lower threshold is set to 0 and it is repeatedly increased of the step size defined by the user until at least N samples
are included in the tail. The same procedure is repeated for the upper tail, starting from a threshold equal to 1.
7. plotpdf_evalfunc.m is the function that plots the estimated pdf of the evaluation function for the chosen output variable.
8. plotpdf_param.m is the function that plots the conditional pdfs of model parameters.
The folder Classes contains the following classes necessary for running the CRA Toolbox:
1. EvaluationFunction.m is the abstract class for a generic evaluation function.
2. Area.m is a concrete class that extends the abstract class EvaluationFunction. It computes the area under the curve of a
model variable.
3. Maximum.m is a concrete class that extends the abstract class EvaluationFunction. It computes the maximum value of a variable curve
4. TimeOfMaximum.m is a concrete class that extends the abstract class EvaluationFunction. It computes the maximum point value of
a variable curve.
5. TailMethod.m is the abstract class for defining a generic method that computes upper and lower tails of a parameter pdf.
The upper tail is conditioned to high values of the evaluation function while the lower tail is conditioned to low values
of the evaluation function.
6. sorted.m is a concrete class that extends the abstract class TailMethod.
7. tmp_sum.m is a concrete class that extends the abstract class TailMethod.
8. TimeBehavior.m is the class representing the time behavior of a model variable.
9. LatinHypercube.m is the class representing the Latin Hypercube generated for perturbing the parameter space.
10. pdfEstimator.m is the class representing an object for estimating a probability density function through the method ksdensity
11. MIRI.m is the class for computing the Moment Independent Robustness Indicator (MIRI) of each model parameter
The folder Examples contains some ODE models in .m format:
- PulseGeneratorNetwork.m
- EGFR_IG1FR_model.m
- model_Peng2016_reduced.m