Perform Sensitivity Analysis

Determine which model components are sensitive to specific conditions or drugs using local and global sensitivity analyses such as Sobol indices, elementary effects, and multiparametric GSA

Perform sensitivity analyses to investigate the influence of model parameters and initial conditions on model behavior. Compute Sobol indices or elementary effects and perform multiparametric global sensitivity analysis (MPGSA) to gain insights into relative contributions of individual parameters that contribute most to the overall model behavior. You can also perform local sensitivity analysis (LSA) to analyze the effect of one model parameter at a time, while keeping the other parameters fixed.

Apps

SimBiology Model Builder	Build QSP, PK/PD, and mechanistic systems biology models interactively
SimBiology Model Analyzer	Analyze QSP, PK/PD, and mechanistic systems biology models

Functions

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Global Sensitivity Analysis: Sobol Indices

`sbiosobol`	Perform global sensitivity analysis by computing first- and total-order Sobol indices (requires Statistics and Machine Learning Toolbox)
`resample`	Resample Sobol indices or elementary effects to new time vector
`addsamples`	Add additional samples to increase accuracy of Sobol indices or elementary effects analysis
`getSimulationResults`	Retrieve model simulation results and sample values used for computing Sobol indices
`plotData`	Plot quantile summary of model simulations from global sensitivity analysis (requires Statistics and Machine Learning Toolbox)
`plot`	Plot first- and total-order Sobol indices and variances
`bar`	Create bar plot of first- and total-order Sobol indices

Global Sensitivity Analysis: Elementary Effects

`sbioelementaryeffects`	Perform global sensitivity analysis (GSA) by computing elementary effects (requires Statistics and Machine Learning Toolbox)
`resample`	Resample Sobol indices or elementary effects to new time vector
`addsamples`	Add additional samples to increase accuracy of Sobol indices or elementary effects analysis
`plotData`	Plot quantile summary of model simulations from global sensitivity analysis (requires Statistics and Machine Learning Toolbox)
`plot`	Plot means and standard deviations of elementary effects
`plotGrid`	Plot parameter grid and points used to compute elementary effects
`bar`	Plot magnitudes of means and standard deviations of elementary effects

Global Sensitivity Analysis: MPGSA

`sbiompgsa`	Perform multiparametric global sensitivity analysis (requires Statistics and Machine Learning Toolbox)
`plotData`	Plot quantile summary of model simulations from global sensitivity analysis (requires Statistics and Machine Learning Toolbox)
`plot`	Plot empirical CDF of multiparametric global sensitivity analysis
`bar`	Create bar plot of multiparametric global sensitivity analysis statistics
`histogram`	Plot histogram of multiparametric global sensitivity analysis results

Local Sensitivity Analysis

`createSimFunction`	Create SimFunction object
`simbio.complexstep.abs`	`abs` function for SimBiology local sensitivity analysis
`simbio.complexstep.max`	`max` function for SimBiology local sensitivity analysis
`simbio.complexstep.min`	`min` function for SimBiology local sensitivity analysis
`sbiosimulate`	Simulate SimBiology model
`getsensmatrix (SimData)`	Get 3-D sensitivity matrix from `SimData` object
`sbioaccelerate`	Prepare model object for accelerated simulations

Objects

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Global Sensitivity Analysis

`SimBiology.gsa.Sobol`	Object containing first- and total-order Sobol indices
`SimBiology.gsa.ElementaryEffects`	Object containing results from calculation of elementary effects for global sensitivity analysis (GSA)
`SimBiology.gsa.MPGSA`	Object containing multiparametric global sensitivity analysis (MPGSA) results

Local Sensitivity Analysis

`SimFunctionSensitivity`	SimFunctionSensitivity object, subclass of `SimFunction` object
`SimFunction`	Function-like interface to execute SimBiology models
`SensitivityAnalysisOptions`	Specify sensitivity analysis options
`Scenarios`	Simulation scenarios
`SimData`	Simulation data
`Configset`	Solver settings information for model simulation
`SolverOptions`	SimBiology model solver options
`RuntimeOptions`	Options for logged species
`CompileOptions`	Dimensional analysis and unit conversion options
`Inputs`	Specify species and parameter input factors for sensitivity analysis
`Normalization`	Specify normalization type for sensitivity analysis
`Outputs`	Specify species and parameter outputs for sensitivity analysis

Topics

Sensitivity Analysis and Simulation Basics

Sensitivity Analysis in SimBiology
Sensitivity analysis lets you explore the effects of variations in model quantities (species, compartments, and parameters) on a model response.
Model Simulation
Simulate dynamic models using various solvers.
Accelerating Model Simulations and Analyses
Accelerate simulation or analysis by converting a model to compiled C code.

App Workflow

Find Important Tumor Growth Parameters with Local Sensitivity Analysis Using SimBiology Model Analyzer
Perform local sensitivity analysis to find important parameters for tumor growth.
Find Important Parameters for Receptor Occupancy with Global Sensitivity Analysis Using SimBiology Model Analyzer
Perform GSA analyses, such as Sobol indices, elementary effects, and multiparametric GSA, to find important model parameters in a target-mediated drug disposition model.

Programmatic Workflow

Perform Global Sensitivity Analysis by Computing First- and Total-Order Sobol Indices
Find sensitive parameters to tumor growth using Sobol indices.
Perform GSA by Computing Elementary Effects
Find sensitive parameters to tumor growth using elementary effects.
Perform Multiparametric Global Sensitivity Analysis (MPGSA)
Find sensitive parameters to receptor occupancy by performing multiparametric global sensitivity analysis.
Calculate Local Sensitivities Using sbiosimulate
Perform local sensitivity analysis on a G-protein model to find parameters that influence the amount of active G protein.