sbiosimulate | Simulate SimBiology model |
createSimFunction | Create SimFunction object |
sbiodose | Construct dose object |
adddose | Add dose object to model |
addobservable | Add observable object to SimBiology model |
sbiovariant | Construct variant object |
addvariant | Add variant to model |
sbiosteadystate | Find steady state of SimBiology model |
sbioaccelerate | Prepare model object for accelerated simulations |
sbiosampleparameters | Generate parameters by sampling covariate model (requires Statistics and Machine Learning Toolbox software) |
sbiosampleerror | Sample error based on error model and add noise to simulation data |
sbioplot | Plot simulation results in one figure |
sbiosubplot | Plot simulation results in subplots |
sbiotrellis | Plot data or simulation results in trellis plot |
sbioensemblerun | Multiple stochastic ensemble runs of SimBiology model |
sbioensembleplot | Show results of ensemble run using 2-D or 3-D plots |
sbioensemblestats | Get statistics from ensemble run data |
Observable | Object containing expression for post-simulation calculations |
Scenarios | Simulation scenarios |
SimFunction object | Function-like interface to execute SimBiology models |
ScheduleDose object | Define drug dosing protocol |
RepeatDose object | Define drug dosing protocol |
Variant object | Store alternate component values |
SimData object | Simulation data |
Configset object | Solver settings information for model simulation |
SolverOptions | Specify model solver options |
RuntimeOptions | Options for logged species |
CompileOptions | Dimensional analysis and unit conversion options |
Model Biological Variability with Virtual Patients Using SimBiology Model Analyzer App
Generate sample values for model parameters to represent virtual patients and simulate to explore model variability.
Simulate Biological Variability of the Yeast G Protein Cycle Using the Wild-Type and Mutant Strains
This example shows how to create and apply a variant to the G protein model of a wild-type strain.
Simulate Model of Glucose-Insulin Response with Different Initial Conditions
This example shows how to simulate the glucose-insulin responses for the normal and diabetic subjects.
Use doses to model different dosing regimens.
Use variants to store alternate parameter values and initial conditions of a model.
Simulate dynamic models using various solvers.
SimBiology® uses a solver function to compute solutions for a system of differential equations at different time intervals during model simulation.
Accelerating Model Simulations and Analyses
Accelerate the simulation or analysis by converting the model to compiled C code.
Combine Simulation Scenarios in SimBiology
Combine generated samples using two different methods.
Troubleshooting Simulation Problems
Troubleshoot SimBiology simulation errors, such as the Integration tolerance not met error, by changing the solver or tolerances.
Selecting Absolute Tolerance and Relative Tolerance for Simulation
SimBiology uses AbsoluteTolerance and RelativeTolerance to control the accuracy of integration during simulation.
For model simulation, SimBiology derives ordinary differential equations (ODEs) from model reactions using mass-balance principles.