Main Content

Simulate Responses to Biological Variability and Doses

Simulate biological variability to compare animal species, strains, or experimental conditions, and investigate different dosing strategies

Represent biological variations among different strains, patients, or experimental conditions by creating model variants and perform Monte Carlo simulations to explore the variability of model parameters that influence a model response or therapeutic endpoint. Simulate virtual patients and alternate scenarios without creating multiple copies of your model. Investigate the efficacy and safety of drugs by simulating doses. Evaluate various dosing regimens and determine the optimal dosing schedules.


SimBiology Model BuilderBuild QSP, PK/PD, and mechanistic systems biology models interactively
SimBiology Model AnalyzerAnalyze QSP, PK/PD, and mechanistic systems biology models


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ScenariosSimulation scenarios
addAdd quantity values, doses, or variants to SimBiology.Scenarios object
generateGenerate scenarios from SimBiology.Scenarios object and return table
getNumberScenariosReturn number of scenarios from SimBiology.Scenarios object
getEntryGet entry contents from SimBiology.Scenarios object
updateEntryUpdate entry contents from SimBiology.Scenarios object
removeRemove entries from SimBiology.Scenarios object
renameRename entry from SimBiology.Scenarios object
verifyVerify SimBiology.Scenarios object
addvariantAdd variant to model
sbiovariantConstruct variant object
addobservableAdd observable object to SimBiology model
addcontent (variant)Append content to variant object
commit (variant)Commit variant contents to model
getvariant (model)Get variant from model
rmcontent (variant)Remove contents from variant object
sbiodoseConstruct dose object
adddoseAdd dose object to model
createDosesCreate dose objects from groupedData object
getdose (model)Return SimBiology dose object
getTable(ScheduleDose,RepeatDose)Return data from SimBiology dose object as table
setTable(ScheduleDose,RepeatDose)Set dosing information from table to dose object
sbiosimulateSimulate SimBiology model
createSimFunctionCreate SimFunction object
sbiosteadystate Find steady state of SimBiology model
sbioacceleratePrepare model object for accelerated simulations
accelerate(SimFunction)Prepare SimFunction object for accelerated simulations
isaccelerated(SimFunction)Determine if SimFunction object is accelerated
addobservableAdd observable expressions to SimData
renameobservableRename observables in SimData
getdata (SimData)Get simulation data from SimData object
removeRemove simulation data from SimData object using expressions
removebynameRemove simulation data by name from SimData object
updateobservableUpdate observable expressions or units in SimData
selectbyname (SimData)Select simulation data by name from SimData object
select (SimData)Select simulation data from SimData object using expressions
sbiosampleparametersGenerate parameters by sampling covariate model (requires Statistics and Machine Learning Toolbox software)
sbiosampleerrorSample error based on error model and add noise to input data
sbioensemblerunMultiple stochastic ensemble runs of SimBiology model
sbioensembleplotShow results of ensemble run using 2-D or 3-D plots
sbioensemblestatsGet statistics from ensemble run data
sbioplotPlot simulation results in one figure
sbiosubplotPlot simulation results in subplots
sbiotrellisPlot data or simulation results in trellis plot


ScenariosSimulation scenarios
SimFunctionFunction-like interface to execute SimBiology models
ScheduleDoseDefine drug dosing protocol
RepeatDoseDefine drug dosing protocol
VariantStore alternate component values
ObservableObject containing expression for post-simulation calculations
SimDataSimulation data
ConfigsetSolver settings information for model simulation
SolverOptionsSpecify model solver options
RuntimeOptionsOptions for logged species
CompileOptionsDimensional analysis and unit conversion options


Doses and Variants Basics

Simulation Basics

App Workflow

Programmatic Workflow


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.

Deriving ODEs from Reactions

For model simulation, SimBiology derives ordinary differential equations (ODEs) from model reactions using mass-balance principles.